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By: MIT xPRO on August 5th, 2020 3 Minute Read

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Here are the Most Common Problems Being Solved by Machine Learning

Machine Learning

Although machine learning offers important new capabilities for solving today’s complex problems, more organizations may be tempted to apply machine learning techniques as a one-size-fits all solution. 

To use machine learning effectively, engineers and scientists need a clear understanding of the most common issues that machine learning can solve. In a recent MIT xPRO Machine Learning whitepaper titled  " Applications For Machine Learning In Engineering and the Physical Sciences,” Professor Youssef Marzouk and fellow MIT colleagues outlined the potentials and limitations of machine learning in STEM. 

Here are some common challenges that can be solved by machine learning:

Accelerate processing and increase efficiency Machine learning can wrap around existing science and engineering models to create fast and accurate surrogates, identify key patterns in model outputs, and help further tune and refine the models. All this helps more quickly and accurately predict outcomes at new inputs and design conditions.

Quantify and manage risk. Machine learning can be used to model the probability of different outcomes in a process that cannot easily be predicted due to randomness or noise. This is especially valuable for situations where reliability and safety are paramount.

Compensate for missing data. Gaps in a data set can severely limit accurate learning, inference, and prediction. Models trained by machine learning improve with more relevant data. When used correctly, machine learning can also help synthesize missing data that round out incomplete datasets.

Make more accurate predictions or conclusions from your data . You can streamline your data-to-prediction pipeline by tuning how your machine learning model’s parameters will be updated and learning during training. Building better models of your data will also improve the accuracy of subsequent predictions.

Solve complex classification and prediction problems. Predicting how an organism’s genome will be expressed or what the climate will be like in fifty years are examples of highly complex problems. Many modern machine learning problems take thousands or even millions of data samples (or far more) across many dimensions to build expressive and powerful predictors, often pushing far beyond traditional statistical methods.

Create new designs. There is often a disconnect between what designers envision and how products are made. It’s costly and time-consuming to simulate every variation of a long list of design variables. Machine learning can identify key variables, automatically generate good options, and help designers identify which best fits their requirements.

Increase yields. Manufacturers aim to overcome inconsistency in equipment performance and predict maintenance by applying machine learning to flag defects and quality issues before products ship to customers, improve efficiency on the production line, and increase yields by optimizing the use of manufacturing resources.

Machine learning is undoubtedly hitting its stride, as engineers and physical scientists leverage the competitive advantage of big data across industries — from aerospace, to construction, to pharmaceuticals, transportation, and energy. But it has never been more important to understand the physics-based models, computational science, and engineering paradigms upon which machine learning solutions are built.

The list above details the most common problems that organizations can solve with machine learning. For more specific applications across engineering and the physical sciences, download MIT xPRO’s free Machine Learning whitepaper .

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Practical Machine Learning with Python

A Problem-Solver's Guide to Building Real-World Intelligent Systems

• © 2018
• Dipanjan Sarkar 0 ,
• Raghav Bali 1 ,
• Tushar Sharma 2

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• A complete guide of theoretical, technical, and hands-on implementations for practical applications of machine learning across diverse domains in the industry
• Shows how data science and machine learning projects are executed in the real world
• Provides readers with the essential skills to tackle their own real-world problems with machine learning

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Table of contents (12 chapters)

Front matter, understanding machine learning, machine learning basics.

• Dipanjan Sarkar, Raghav Bali, Tushar Sharma

The Python Machine Learning Ecosystem

The machine learning pipeline, processing, wrangling, and visualizing data, feature engineering and selection, building, tuning, and deploying models, real-world case studies, analyzing bike sharing trends, analyzing movie reviews sentiment, customer segmentation and effective cross selling, analyzing wine types and quality, analyzing music trends and recommendations, forecasting stock and commodity prices, deep learning for computer vision, back matter.

• Machine Learning
• Natural Language Processing
• Deep Learning
• Social network analysis
• recommender systems
• image processing
• trend analysis

About this book

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.

Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries andframeworks are also covered.

Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem.

Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today!

• Execute end-to-end machine learning projects and systems
• Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks
• Review case studies depicting applications of machine learning and deep learning on diverse domains and industries
• Apply a wide range of machine learning models including regression, classification, and clustering.
• Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning.

Authors and Affiliations

Embassy paragon, site no. 6/2 & 6/3, intel technology india pvt ltd embassy paragon, site no. 6/2 & 6/3, bangalore, india.

Dipanjan Sarkar

Raghav Bali, Tushar Sharma

About the authors

Dipanjan Sarkar  is a Data Scientist at Intel, on a mission to make the world more connected and productive. He primarily works on data science, analytics, business intelligence, application development, and building large-scale intelligent systems. He holds a master of technology degree in Information Technology with specializations in Data Science and Software Engineering from the International Institute of Information Technology, Bangalore. He is also an avid supporter of self-learning, especially Massive Open Online Courses and also holds a Data Science Specialization from Johns Hopkins University on Coursera. 

  Raghav Bali has a master's degree (gold medalist) in Information

Raghav is a technology enthusiast who loves reading and playing around with new gadgets and technologies. He has also authored several books on R, Machine Learning and Analytics. He is a shutterbug, capturing moments when he isn't busy solving problems.

Apart from work Tushar enjoys watching movies, playing badminton and is an avid reader. He has also authored a book on R and social media analytics.

Bibliographic Information

Book Title : Practical Machine Learning with Python

Book Subtitle : A Problem-Solver's Guide to Building Real-World Intelligent Systems

Authors : Dipanjan Sarkar, Raghav Bali, Tushar Sharma

DOI : https://doi.org/10.1007/978-1-4842-3207-1

Publisher : Apress Berkeley, CA

eBook Packages : Professional and Applied Computing , Apress Access Books , Professional and Applied Computing (R0)

Copyright Information : Dipanjan Sarkar, Raghav Bali and Tushar Sharma 2018

Softcover ISBN : 978-1-4842-3206-4 Published: 22 December 2017

eBook ISBN : 978-1-4842-3207-1 Published: 20 December 2017

Edition Number : 1

Number of Pages : XXV, 530

Topics : Artificial Intelligence , Python , Open Source

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• What Is Machine Learning? | A Beginner’s Guide

What Is Machine Learning? | A Beginner's Guide

Published on June 27, 2023 by Kassiani Nikolopoulou . Revised on August 4, 2023.

Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on developing methods for computers to learn and improve their performance. It aims to replicate human learning processes, leading to gradual improvements in accuracy for specific tasks. The main goals of ML are:

• classifying data based on models that have been developed (e.g., detecting spam emails).
• making predictions regarding some future outcome on the basis of these models (e.g., predicting house prices in a city.)

Machine learning has a wide range of applications, including language translation, consumer preference predictions, and medical diagnoses

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Table of contents

What is machine learning, how does machine learning work, types of machine learning models, machine learning vs. deep learning, advantages & limitations of machine learning, other interesting articles, frequently asked questions about machine learning.

Machine learning is a set of methods that computer scientists use to train computers how to learn. Instead of giving precise instructions by programming them, they give them a problem to solve and lots of examples (i.e., combinations of problem-solution) to learn from.

For example, a computer may be given the task of identifying photos of cats and photos of trucks. For humans, this is a simple task, but if we had to make an exhaustive list of all the different characteristics of cats and trucks so that a computer could recognize them, it would be very hard. Similarly, if we had to trace all the mental steps we take to complete this task, it would also be difficult (this is an automatic process for adults, so we would likely miss some step or piece of information).

Instead, ML teaches a computer in a way similar to how toddlers learn: by showing the computer a vast amount of pictures labeled as “cat” or “truck,” the computer learns to recognize the relevant features that constitute a cat or a truck. From that point onwards, the computer can recognize trucks and cats from photos it has never “seen” before (i.e., photos that were not used to train the computer).

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Performing machine learning involves a series of steps:

• Data collection . Machine learning starts with gathering data from various sources, such as music recordings, patient histories, or photos.This raw data is then organized and prepared for use as training data, which is the information used to teach the computer.
• Data preparation. Preparing the raw data involves cleaning the data , removing any errors, and formatting it in a way that the computer can understand. It also involves feature engineering or feature extraction , which is selecting relevant information or patterns that can help the computer solve a specific task. It is important that engineers use large datasets so that the training information is sufficiently varied and thus representative of the population or problem.
• Choosing and training the model. Depending on the task at hand, engineers choose a suitable machine learning model and start the training process. The model is like a tool that helps the computer make sense of the data. During training, the computer model automatically learns from the data by searching for patterns and adjusting its internal settings. It essentially teaches itself to recognize relationships and make predictions based on the patterns it discovers.
• Model optimization. Human experts can enhance the model’s accuracy by adjusting its parameters or settings. By experimenting with various configurations, programmers try to optimize the model’s ability to make precise predictions or identify meaningful patterns in the data.
• Model evaluation. Once the training is over, engineers need to check how well it performs. To do this, they use separate data that were not included in the training data and therefore are new to the model. This evaluation data allows them to test how well the model can generalize what it has learned (i.e., apply it to new data it has never encountered before). This also provides engineers with insights for further improvements.
• Model deployment. After the model has been trained and evaluated, it is used to make predictions or identify patterns in new, unseen data. For example, we use new images of vehicles and animals as input and, after analyzing them, the trained model can classify the image as either “truck” or “cat.” The model continues to adjust automatically to improve its performance.

It is important to keep in mind that ML implementation goes through an iterative cycle of building, training, and deploying a machine learning model: each step of the entire ML cycle is revisited until the model has gone through enough iterations to learn from the data. The goal is to obtain a model that can perform equally well on new data.

Machine learning models are created by training algorithms on large datasets.There are three main approaches or frameworks for how a model learns from the training data:

• Supervised learning is used when the training data consist of examples that are clearly described or labeled. Here, the algorithm has a “supervisor” (i.e., a human expert who acts like a teacher and gives the computer the correct answers). The human expert has already prepared the data and labeled them, for example, into pictures of trucks and cats, which the algorithm uses to learn. Since the answers are included in the data, the algorithm can “see” how accurate its answers are and improve over time. Supervised learning is used for classification tasks (e.g., filtering spam emails) and prediction tasks (e.g., the future price of a stock).
• Unsupervised learning is used when the training data is unlabeled. The aim is to explore and discover patterns, structures, or relationships in the data without specific guidance. Clustering is the most common unsupervised learning task. It is a form of classification without predefined classes. It involves categorizing data into classes based on features hidden within the data (e.g., segmenting a market into types of customers). Here, the algorithm tries to find similar objects and puts them together in a cluster or group, without human intervention.
• Reinforcement learning (RL) is a different approach where the computer program learns by interacting with an environment. Here, the task or problem is not related to data, but to an environment such as a video game or a city street (in the context of self-driving cars). Through trial and error, this approach allows computer programs to automatically determine the best actions within a certain context to optimize their performance. The computer receives feedback in the form of reward or punishment based on its actions and gradually learns how to play a game or drive in a city.

Finding the right algorithm

Algorithms provide the methods for supervised, unsupervised, and reinforcement learning. In other words, they dictate how exactly models learn from data, make predictions or classifications, or discover patterns within each learning approach.

Finding the right algorithm is to some extent a trial-and-error process, but it also depends on the type of data available, the insights you want to to get from the data, and the end goal of the machine learning task (e.g., classification or prediction). For example, a linear regression algorithm is primarily used in supervised learning for predictive modeling, such as predicting house prices or estimating the amount of rainfall.

Machine learning and deep learning are both subfields of artificial intelligence. However, deep learning is in fact a subfield of machine learning. The main difference between the two is how the algorithm learns:

• Machine learning requires human intervention. An expert needs to label the data and determine the characteristics that distinguish them. The algorithm then can use these manually extracted characteristics or features to create a model.
• Deep learning doesn’t require a labeled dataset. It can process unstructured data like photos or texts and automatically determine which features are relevant to sort data into different categories.

In other words, we can think of deep learning as an improvement on machine learning because it can work with all types of data and reduces human dependency.

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Machine learning is a powerful problem-solving tool. However, it also has its limitations. Listed below are the main advantages and current challenges of machine learning:

• Scale of data. Machine learning can handle problems that require processing massive volumes of data. ML models can discover patterns and make predictions on their own, offering insights that traditional programming can’t offer.
• Flexibility. Machine learning models can adapt to new data and continuously improve their accuracy over time. This is invaluable when it comes to dynamic data that constantly changes, such as movie recommendations, which are based on the last movie you watched or what you are currently watching.
• Automation. Machine learning models eliminate manual data analysis and interpretation and ultimately automate decision-making.This applies to complex tasks and large amounts of data that human experts would never be able to process or complete, such as going through recordings from conversations with customers. In other cases, ML can undertake tasks that humans would be able to complete, such as finding an answer to a question, but never on that scale or as efficiently as an online search engine.

Limitations

• Overfitting and generalization issues. When a machine learning model becomes too accustomed to the training data, it cannot generalize to examples it hasn’t encountered before (this is called “overfitting”). This means that the model is so specific to the original data, that it might fail to correctly classify or make predictions on the basis of new, unseen data. This results in erroneous outcomes and less-than-optimal decisions.
• Explainability. Some machine learning models operate like a “black box” and not even experts are able to explain why they arrived at a certain decision or prediction. This lack of explainability and transparency can be problematic in sensitive domains like finance or health, and raises issues around accountability. Imagine, for example, if we couldn’t explain why a bank loan had been refused or why a specific treatment had been recommended.
• Algorithmic bias. Machine learning models train on data created by humans. As a result, datasets can contain biased, unrepresentative information. This leads to algorithmic bias : systematic and repeatable errors in a ML model which create unfair outcomes, such as privileging one group of job applicants over another.

If you want to know more about ChatGPT, AI tools , fallacies , and research bias , make sure to check out some of our other articles with explanations and examples.

• ChatGPT vs human editor
• ChatGPT citations
• Legal implications ChatGPT
• Using ChatGPT to write an essay
• Sunk cost fallacy
• Straw man fallacy
• Slippery slope fallacy
• Red herring fallacy
• Ecological fallacy
• Logical fallacy

Research bias

• Implicit bias
• Framing bias
• Cognitive bias
• Optimism bias
• Hawthorne effect
• Unconscious bias

Although the terms artificial intelligence and machine learning are often used interchangeably, they are distinct (but related) concepts:

• Artificial intelligence is a broad term that encompasses any process or technology aiming to build machines and computers that can perform complex tasks typically associated with human intelligence, like decision-making or translating.
• Machine learning is a subfield of artificial intelligence that uses data and algorithms to teach computers how to learn and perform specific tasks without human interference.

In other words, machine learning is a specific approach or technique used to achieve the overarching goal of AI to build intelligent systems.

Traditional programming and machine learning are essentially different approaches to problem-solving.

In traditional programming, a programmer manually provides specific instructions to the computer based on their understanding and analysis of the problem. If the data or the problem changes, the programmer needs to manually update the code.

In contrast, in machine learning the process is automated: we feed data to a computer and it comes up with a solution (i.e. a model) without being explicitly instructed on how to do this. Because the ML model learns by itself, it can handle new data or new scenarios.

Overall, traditional programming is a more fixed approach where the programmer designs the solution explicitly, while ML is a more flexible and adaptive approach where the ML model learns from data to generate a solution.

A real-life application of machine learning is an email spam filter. To create such a filter, we would collect data consisting of various email messages and features (subject line, sender information, etc.) which we would label as spam or not spam. We would then train the model to recognize which features are associated with spam emails. In this way, the ML model would be able to classify any incoming emails as either unwanted or legitimate.

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

Nikolopoulou, K. (2023, August 04). What Is Machine Learning? | A Beginner's Guide. Scribbr. Retrieved April 9, 2024, from https://www.scribbr.com/ai-tools/machine-learning/

Theobald, O. (2021). Machine Learning for Absolute Beginners: A Plain English Introduction (3rd Edition).

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How to Approach Machine Learning Problems

How do you approach machine learning problems? Are neural networks the answer to nearly every challenge you may encounter?

In this article, Toptal Freelance Python Developer Peter Hussami explains the basic approach to machine learning problems and points out where neural may fall short.

By Peter Hussami

Peter’s rare math-modeling expertise includes audio and sensor analysis, ID verification, NPL, scheduling, routing, and credit scoring.

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One of the main tasks of computers is to automate human tasks. Some of these tasks are simple and repetitive, such as “move X from A to B.” It gets far more interesting when the computer has to make decisions about problems that are far more difficult to formalize. That is where we start to encounter basic machine learning problems.

Historically, such algorithms were built by scientists or experts that had intimate knowledge of their field and were largely based on rules. With the explosion of computing power and the availability of large and diverse data sets, the focus has shifted toward a more computational approach.

Most popularized machine learning concepts these days have to do with neural networks, and in my experience, this created the impression in many people that neural networks are some kind of a miracle weapon for all inference problems. Actually, this is quite far from the truth. In the eyes of the statistician, they form one class of inference approaches with their associated strengths and weaknesses, and it completely depends on the problem whether neural networks are going to be the best solution or not.

Quite often, there are better approaches.

In this article, we will outline a structure for attacking machine learning problems. There is no scope for going into too much detail about specific machine learning models , but if this article generates interest, subsequent articles could offer detailed solutions for some interesting machine learning problems.

First, however, let us spend some effort showing why you should be more circumspect than to automatically think “ neural network ” when faced with a machine learning problem.

Pros and Cons of Neural Networks

With neural networks, the inference is done through a weighted “network.” The weights are calibrated during the so-called “learning” process, and then, subsequently, applied to assign outcomes to inputs.

As simple as this may sound, all the weights are parameters to the calibrated network, and usually, that means too many parameters for a human to make sense of.

So we might as well just consider neural networks as some kind of an inference black box that connects the input to output, with no specific model in between.

Let us take a closer look at the pros and cons of this approach.

Advantages of Neural Networks

• The input is the data itself. Usable results even with little or no feature engineering.
• Trainable skill. With no feature engineering, there is no need for such hard-to-develop skills as intuition or domain expertise. Standard tools are available for generic inferences.
• Accuracy improves with the quantity of data. The more inputs it sees, the better a neural network performs.
• May outperform classical models when there is not full information about the model. Think of public sentiment, for one.
• Open-ended inference can discover unknown patterns. If you use a model and leave a consideration out of it, it will not detect the corresponding phenomenon. Neural networks might.

Successful neural network example: Google’s AI found a planet orbiting a distant star—where NASA did not—by analyzing accumulated telescope data.

Disadvantages of Neural Networks

• They require a lot of (annotated!) data. First, this amount of data is not always available. Convergence is slow. A solid model (say, in physics) can be calibrated after a few observations—with neural networks, this is out of the question. Annotation is a lot of work, not to mention that it, in itself, is not foolproof.
• No information about the inner structure of the data. Are you interested in what the inference is based on? No luck here. There are situations where manually adjusting the data improves inference by a leap, but a neural network will not be able to help with that.
• Overfitting issues. It happens often that the network has more parameters than the data justifies, which leads to suboptimal inference.
• Performance depends on information. If there is full information about a problem, a solid model tends to outperform a neural network.
• There are sampling problems. Sampling is always a delicate issue, but with a model, one can quickly develop a notion of problematic sampling. Neural networks learn only from the data, so if they get biased data, they will have biased conclusions.

An example of failure: A personal relation told me of a major corporation (that I cannot name) that was working on detecting military vehicles on aerial photos. They had images where there were such vehicles and others that did not. Most images of the former class were taken on a rainy day, while the latter were taken in sunny weather. As a result, the system learned to distinguish light from shadow.

To sum up, neural networks form one class of inference methods that have their pros and cons.

The fact that their popularity outshines all other statistical methods in the eyes of the public has likely more to do with corporate governance than anything else.

Training people to use standard tools and standardized neural network methods is a far more predictable process than hunting for domain experts and artists from various fields. This, however, does not change the fact that using a neural network for a simple, well-defined problem is really just shooting a sparrow with a cannon: It needs a lot of data, requires a lot of annotation work, and in return might just underperform when compared to a solid model. Not the best package.

Still, there is huge power in the fact that they “democratize” statistical knowledge. Once a neural network-based inference solution is viewed as a mere programming tool, it may help even those who don’t feel comfortable with complex algorithms. So, inevitably, a lot of things are now built that would otherwise not exist if we could only operate with sophisticated models.

Approaching Machine Learning Problems

When approaching machine learning problems, these are the steps you will need to go through:

• Setting acceptance criteria
• Cleaning your data and maximizing ist information content
• Choosing the most optimal inference approach
• Train, test, repeat

Let us see these items in detail.

Setting Acceptance Criteria

You should have an idea of your target accuracy as soon as possible, to the extent possible. This is going to be the target you work towards.

Cleansing Your Data and Maximizing Its Information Content

This is the most critical step. First of all, your data should have no (or few) errors. Cleansing it of these is an essential first step. Substitute missing values, try to identify patterns that are obviously bogus, eliminate duplicates and any other anomaly you might notice.

As for information, if your data is very informative (in the linear sense), then practically any inference method will give you good results. If the required information is not in there, then the result will be noise. Maximizing the information means primarily finding any useful non-linear relationships in the data and linearizing them. If that improves the inputs significantly, great. If not, then more variables might need to be added. If all of this does not yield fruit, target accuracy may suffer.

With some luck, there will be single variables that are useful. You can identify useful variables if you—for instance—plot them against the learning target variable(s) and find the plot to be function-like (i.e., narrow range in the input corresponds to narrow range in the output). This variable can then be linearized—for example, if it plots as a parabola, subtract some values and take the square root.

For variables that are noisy—narrow range in input corresponds to a broad range in the output—we may try combining them with other variables.

To have an idea of the accuracy, you may want to measure conditional class probabilities for each of your variables (for classification problems) or to apply some very simple form of regression, such as linear regression (for prediction problems). If the information content of the input improves, then so will your inference, and you simply don’t want to waste too much time at this stage calibrating a model when the data is not yet ready. So keep testing as simple as possible.

Choosing the Most Optimal Inference Approach

Once your data is in decent shape, you can go for the inference method (the data might still be polished later, if necessary).

Should you use a model? Well, if you have good reason to believe that you can build a good model for the task, then you probably should. If you don’t think so, but there is ample data with good annotations, then you may go hands-free with a neural network. In practical machine learning applications, however, there is often not enough data for that.

Playing accuracy vs. cover often pays off tremendously. Hybrid approaches are usually completely fine. Suppose the data is such that you can get near-100% accuracy on 80% of it with a simple model? This means you can demonstrable results quickly, and if your system can identify when it’s operating on the 80% friendly territory, then you’ve basically covered most of the problem. Your client may not yet be fully happy, but this will earn you their trust quickly. And there is nothing to prevent you from doing something similar on the remaining data: with reasonable effort now you cover, say, 92% of the data with 97% accuracy. True, on the rest of the data, it’s a coin flip, but you already produced something useful.

For most practical applications, this is very useful. Say, you’re in the lending business and want to decide whom to give a loan, and all you know is that on 70% of the clients your algorithm is very accurate. Great—true, the other 30% of your applicants will require more processing, but 70% can be fully automated. Or: suppose you’re trying to automate operator work for call centers, you can do a good (quick and dirty) job on the most simple tasks only, but these tasks cover 50% of the calls? Great, the call center saves money if they can automate 50% of their calls reliably.

To sum up: If the data is not informative enough, or the problem is too complex to handle in its entirety, think outside the box. Identify useful and easy-to-solve sub-problems until you have a better idea.

Once you have your system ready, learn, test and loop it until you’re happy with the results.

Train, Test, Repeat

After the previous steps, little of interest is left. You have the data, you have the machine learning method, so it’s time to extract parameters via learning and then test the inference on the test set. Literature suggests 70% of the records should be used for training, and 30% for testing.

If you’re happy with the results, the task is finished. But, more likely, you developed some new ideas during the procedure, and these could help you notch up in accuracy. Perhaps you need more data? Or just more data cleansing? Or another model? Either way, chances are you’ll be busy for quite a while.

So, good luck and enjoy the work ahead!

Further Reading on the Toptal Blog:

• Identifying the Unknown With Clustering Metrics
• Advantages of AI: Using GPT and Diffusion Models for Image Generation
• Stars Realigned: Improving the IMDb Rating System
• Machines and Trust: How to Mitigate AI Bias

Understanding the basics

Machine learning vs. deep learning: what's the difference.

Machine learning includes all inference techniques while deep learning aims at uncovering meaningful non-linear relationships in the data. So deep learning is a subset of machine learning and also a means of automated feature engineering applied to a machine learning problem.

Which language is best for machine learning?

The ideal choice is a language that has both broad programming library support and allows you to focus on the math rather than infrastructure. The most popular language is Python, but algorithmic languages such as Matlab or R or mainstreamers like C++ and Java are all valid choices as well.

Machine learning vs. neural networks: What's the difference?

Neural networks represent just one approach within machine learning with its pros and cons as detailed above.

What is the best way to learn machine learning?

There are some good online courses and summary pages. It all depends on one’s skills and tastes. My personal advice: Think of machine learning as statistical programming. Beef up on your math and avoid all sources that equate machine learning with neural networks.

What are the advantages and disadvantages of artificial neural networks?

Some advantages: no math, feature engineering, or artisan skills required; easy to train; may uncover aspects of the problem not originally considered. Some disadvantages: requires relatively more data; tedious preparation work; leaves no explanation as to why they decide the way they do, overfitting.

• NeuralNetwork
• MachineLearning

Peter Hussami

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Solve Problems With Machine Learning Effectively in Four Steps

The projected $15.7 trillion contribution of Artificial Intelligence by 2030 highlights its transformative potential, driving widespread adoption across sectors. As we approach this revolution, we will need to figure out which tasks can be effectively tackled with Machine Learning (ML). So, how do you effectively solve problems with Machine Learning (ML)?

Effective problem-solving with Machine Learning boils down to four key factors:

• Environment

Solve Problems with Machine Learning: Yay or Nay?

In today’s business landscape, data reigns supreme, but do we really need AI to navigate its complexities? While it may seem appealing to tout AI-powered solutions, the reality is often more nuanced. Thus, there’s a growing need to educate ourselves on the problems that AI and machine learning (ML) can effectively solve.

Google suggests focusing on problem understanding before diving into AI development, ensuring that solutions are tailored to real-world challenges. Rather than rushing to architect algorithms and make assumptions about data attributes and volume, it’s essential to dedicate ample time to understanding the problem statement thoroughly.

Step One: Identify Patterns to Solve Problems with Machine Learning

The concept of a pattern revolves around using AI to analyze data effectively. Once you thoroughly understand the problem, you start exploring potential solutions. This involves testing different versions of the model to uncover weaknesses and refine it further.

Documenting your progress is crucial, as it helps capture both successful outcomes and lessons learned from failures. This accumulated knowledge guides your intuition as a data scientist when identifying solvable problems for ML.

Another key aspect is data availability. Without data, even the most advanced models are useless. If necessary, you can use publicly available data or simulate data for your business case. However, the model’s predictive power relies heavily on having relevant attributes with significant signals.

The quantity of data needed depends on the complexity of the ML task. Machine Learning’s effectiveness to solve a problem correlates directly with data quality, so it’s essential to include only relevant features and eliminate noise.

Choosing the best model from numerous hypotheses and experiments can be challenging. Ideally, the best model generalizes well to unseen data. Creating an evaluation dataset that mirrors real-world scenarios is crucial for assessing the model’s performance in production. Running the best model on this dataset helps determine its true efficacy.

Step 2: Building Machine Learning Models for Environments

What is the environment? Simply put, it is the setting for which is Machine Learning Model is built. Developing a machine learning model for dynamic environments is tricky. A dymanic environment where there are multiple variables. It’s crucial to set realistic expectations for the model.

Creating models for such dynamic environments requires deep knowledge of the field. It’s not just about fancy algorithms but also understanding the intricacies of the environment in question. So, effective ML models here need both technical skills and nsights.

Let us look at this with an example.

Problem-Solving with Machine Learning: Predicting Stock Exchange Prices

• There will be constantly changing factors, both micro and macro-economic. Stock prices fluctuate due to various factors like market sentiment, economic indicators, and news events.
• Realistic expectations involve comparing model performance to what an average person with basic stock market knowledge could achieve, rely on basic patterns to make decisions.
• An unrealistic expectation would be to expect the model to give results akin to a seasoned expert who has learned nuances and intuitions through repeated trial and error.

Did you know there are many Open Source ML environments available for you to get started?

Step 3: Validate the Machine Learning Model

Before beginning the modeling process, it’s crucial to understand how the model’s performance relates to business objectives. Scientific metrics like Precision, Recall, RMSE, and ROC-AUC are commonly used to assess model performance, but it’s essential to consider their impact on business outcomes.

For example, achieving an 80% Precision score may seem good, but how does it affect conversion rates and revenue?Validation of model predictions is important. This involves establishing a consensus on how human experts will evaluate the model’s output.

In essence, it’s about ensuring that the model’s performance metrics align with real-world business goals and that its predictions are evaluated in a meaningful way by experts.

Step Four: Consider the Dimensions

The fourth factor you need to consider before applying AI and ML to problem-solving is the dimensions. As more factors are considered, the estimate becomes more accurate but performing calculations manually becomes challenging. Machine learning excels in mining data associations on large datasets, providing more accurate predictions compared to human experts, making it easier for you to solve problems.

Let’s also consider this with an example.

Problem-Solving with Machine Learning: Predicting Housing Market Trends

Estimating house market value involves considering factors like area, number of rooms, and age of the house. Initially, correlating price with area gives a rough estimate but may not be accurate. Considering additional factors like number of rooms, age of the house, proximity to amenities like schools and medical facilities improves accuracy.

Would you like to start learning how Artificial Intelligence and Machine Learning work? If yes, don’t leave without taking a look at the courses we offer!

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You can master Computer Vision, Deep Learning, and OpenCV - PyImageSearch

Algorithms Artificial Intelligence Data Science Deep Learning Machine Learning

Exploring the Landscape of Machine Learning: Techniques, Applications, and Insights

by Hector Martinez on April 1, 2024

Table of Contents

Introduction: the power of machine learning in modern industries, what is machine learning, understanding the core types of machine learning techniques, supervised learning: from basics to real-world applications explained, unsupervised learning explained: discovering hidden patterns, bridging the gap with semi-supervised learning: enhancing data understanding, core machine learning techniques for business innovation, deep learning: unleashing the power of neural networks, leveraging transfer learning for efficient ai development, federated learning: privacy-preserving machine learning, meta-learning: teaching ai to learn more effectively, deep learning breakthroughs, generative adversarial networks (gans): innovations in synthetic data, transformers in nlp: beyond conventional models, reinforced learning: strategies for a model to learn from interaction, machine learning for solving real-world problems, understanding different machine learning problem types, solving classification problems with machine learning, solving regression problems through machine learning techniques, clustering problems: unsupervised learning approaches, detecting anomalies: unsupervised learning for anomaly detection, optimizing decision-making with reinforcement learning: strategies and applications, leveraging machine learning for strategic advantages across industries, exploring the backbone of ai: a guide to machine learning algorithms, comprehensive guide to machine learning algorithms, decision trees: key to classification and regression, random forests for enhanced prediction accuracy, support vector machines (svm) in machine learning.

• Neural Networks: The Brain Behind AI’s Decision-Making

K-Nearest Neighbors (KNN): A Go-To Algorithm for Precision

Principal component analysis (pca): simplifying data with dimensionality reduction, clustering algorithms: grouping data with machine learning, the critical role of labels in machine learning algorithms, harnessing semi-supervised learning to reduce labeling costs, exploring unsupervised learning: beyond labels, maximizing rewards with reinforcement learning, leveraging analytical learning for data-driven decisions, high-dimensional data with analytical models, summary: mastering machine learning for real-world solutions.

The field of machine learning is taking the world by storm, revolutionizing various industries that range from healthcare to finance to transportation. With the massive amounts of data that businesses and organizations now generate, machine learning algorithms have become a critical tool for extracting insights and making informed decisions. There are different types of machine learning available, each with its own unique advantages and drawbacks. In this article, we’ll delve into the four primary forms of machine learning: supervised learning, unsupervised learning, semi-supervised learning, and reinforced learning.

Note: This blog post is meant to be a guide to the ever-changing landscape of AI and Machine Learning. If you already have some familiarity with fundamental topics, you probably don’t need this, and can check out some of our more advanced blog posts here .

Machine learning (ML) is a type of artificial intelligence (AI) that’s focused on creating algorithms that can learn from data and improve their performance over time. Instead of explicitly programming them for every task, machine learning algorithms are designed to automatically identify patterns in data and use those patterns to make predictions or decisions.

To get a more grounded, code-first introduction to machine learning, read here .

This is the most common type of machine learning, and it is used when the data is labeled. In this case, the algorithm learns to map inputs to outputs based on examples of labeled data. The input data is referred to as features, and the output data is referred to as the label or target. Supervised learning aims to use these labeled examples to train the algorithm to make accurate predictions on new, unlabeled data.

There are two main types of supervised learning: classification and regression. Classification is used when the output is a categorical variable, and the algorithm needs to predict the category where the input data belongs. Examples of classification tasks include image recognition, sentiment analysis, and spam detection. Regression is used when the output is a continuous variable, and the algorithm needs to predict a numerical value. Examples of regression tasks include predicting housing prices, weather forecasting, and stock market analysis.

Unsupervised learning is used when the data is unlabeled. In this case, the algorithm learns to find patterns and relationships in the data without explicit guidance. Unsupervised learning aims to explore the data structure and discover any hidden patterns or groupings.

Several types of unsupervised learning include clustering, dimensionality reduction, and anomaly detection. Clustering is used to group similar data points based on their similarities, while dimensionality reduction is used to reduce the number of features in the data to simplify the problem. Finally, anomaly detection is used to identify unusual data points that do not fit the normal patterns of the data.

Semi-supervised learning is used when the data is partially labeled. In this case, the algorithm uses labeled and unlabeled data to make predictions. Semi-supervised learning aims to use the labeled data to guide the learning process and improve the accuracy of the predictions.

Semi-supervised learning is often used when data labeling is expensive or time-consuming, such as in medical imaging or natural language processing. By using the available labeled data to guide the learning process, semi-supervised learning can achieve high levels of accuracy with less labeled data than would be required for supervised learning.

Machine learning is a powerful tool that can help businesses and organizations make better decisions and gain new insights into their data. Understanding the different types of machine learning is essential for choosing the right approach for a given problem. Whether you are working with labeled or unlabeled data, or whether you need to learn through trial and error, there is a type of machine learning that can help you achieve your goals.

The field of machine learning is advancing rapidly, and new techniques and algorithms are being developed at an ever-increasing rate. In this blog post, we will explore some of the latest and cutting-edge machine learning techniques that are currently making waves in the industry.

Some of our tutorials provide you with the tools and techniques required for business innovation in the field of Deep Learning and Computer Vision.

1. Deep Learning

• Self-Driving Cars: Deep learning algorithms excel at object detection and recognition, which is crucial for self-driving cars to navigate safely. They can identify pedestrians, vehicles, traffic signs, and more in real-time.
• Medical Diagnosis: Deep learning can analyze medical images like X-rays, mammograms, and MRIs to detect abnormalities or diseases, aiding doctors in diagnosis and treatment planning.
• Facial Recognition: Deep learning powers facial recognition systems used for security purposes, access control, and even personalized marketing.

2. Embedded Systems

• Internet of Things (IoT): Embedded systems equipped with computer vision capabilities can be used in smart homes for tasks like object recognition (security cameras) or facial recognition (smart door locks).
• Industrial Automation: Embedded systems with machine learning can perform real-time quality control in factories, identify defects in products, or predict equipment maintenance needs.
• Robotics: Embedded systems with computer vision allow robots to navigate their environment, identify objects for manipulation, and interact with the physical world more intelligently.

3. Optical Character Recognition (OCR)

• Document Automation: OCR can automate data entry tasks by extracting text from scanned documents, invoices, or receipts, saving time and reducing errors.
• Self-Service Systems: Libraries and banks use OCR scanners to automate book check-in/out or process checks for deposit.
• Accessibility Tools: OCR technology can convert printed text into audio for visually impaired individuals, making documents and information more accessible.

4. Machine Learning

• Recommendation Systems: Machine-learning algorithms power recommendation systems on e-commerce platforms or streaming services, suggesting products or content users might be interested in.
• Fraud Detection: Machine learning can analyze financial transactions to identify fraudulent activity in real-time, protecting users from financial harm.
• Spam Filtering: Machine learning algorithms can analyze email content to identify and filter spam messages, keeping your inbox clean and organized.

These are just a few examples, and the potential applications of these technologies continue to grow as computer vision and machine learning advancements accelerate. Explore these resources on PyImageSearch to delve deeper into the practical implementations of these techniques in various real-world scenarios.

Deep learning is a subset of machine learning based on artificial neural networks. It has been a hot topic in the machine-learning community for several years. It has been used in a wide range of applications, from speech recognition to image classification to natural language processing.

The key advantage of deep learning is its ability to learn and extract features from large, complex datasets. This is achieved by building a hierarchy of neural networks, where each layer extracts increasingly complex features from the input data. Deep learning has also been shown to outperform traditional machine learning algorithms in many tasks.

Transfer learning is a technique that allows a pre-trained model to be used for a new task with minimal additional training. This is achieved by leveraging the knowledge that the pre-trained model has already learned and transferring it to the new task. Read more about the practical aspects of Transfer Learning in the tutorial from Figure 1 .

Transfer learning has become popular in recent years because it can significantly reduce the data and training time required for a new task. It has been used in a wide range of applications, including natural language processing, image recognition, and speech recognition. Here’s how it can be applied to various applications:

1. Object Detection

• Pre-trained Models: Popular choices include VGG16, ResNet50, or InceptionV3 trained on ImageNet (a massive image dataset with thousands of object categories).
• Freeze the initial layers of the pre-trained model (these layers learn generic features like edges and shapes).
• Add new layers on top specifically designed for object detection (like bounding box prediction).
• Train the new layers with your custom object detection dataset.
• Benefits: Significantly reduces training time compared to training from scratch and leverages pre-learned features for better object detection accuracy.

2. OCR (Optical Character Recognition)

• Pre-trained Models: These are trained on large text datasets like MNIST (handwritten digits) or COCO-Text (images with text captions).
• Freeze the initial layers responsible for extracting low-level image features.
• Add new layers (e.g., convolutional layers) specifically designed for character recognition.
• Train the new layers with your custom dataset, which contains images of the specific text format you want to recognize (e.g., invoices, receipts, license plates).
• Benefits: Faster training and improved accuracy for recognizing specific text formats compared to training from scratch.

3. Image Classification

• Pre-trained Models: Similar to object detection, models like VGG16 or ResNet50 can be used.
• Freeze the initial layers of the pre-trained model.
• Add a new fully connected layer at the end with the number of neurons matching your classification categories.
• Train the new layer with your custom image dataset labeled for your specific classification task (e.g., classifying types of flowers and different breeds of dogs).
• Benefits: Reduces training time and leverages pre-learned features for improved image classification accuracy on new datasets.

Additional Points:

• Fine-tuning the Model: To achieve optimal results, you can adjust the learning rate of the newly added layers compared to the frozen pre-trained layers.
• Transfer Learning Limitations: While powerful, transfer learning might not be ideal for entirely new visual concepts not present in the pre-trained model’s training data. In such cases, custom model training from scratch might be necessary.

By leveraging transfer learning, we can achieve significant performance improvements in various computer vision tasks with less training data and computational resources.

Federated learning is a technique that allows multiple devices to collaboratively learn a model without sharing their data. This is achieved by training the model locally on each device and then aggregating the results to create a global model.

Federated learning has become popular in applications where data privacy is a concern, such as healthcare and finance. It allows models to be trained on data that cannot be centralized, such as data stored on individual devices or in different geographic locations.

Meta-learning is a technique that allows a model to learn how to learn. This is achieved by training the model on a variety of tasks and environments so it can quickly adapt to new tasks and environments.

Meta-learning has been used in a wide range of applications, from computer vision to natural language processing. It has the potential to significantly reduce the amount of training data and time required for a new task, making it a powerful tool for machine learning.

These are just a few of the many new and cutting-edge machine-learning techniques being developed. As the field of machine learning continues to advance, we can expect to see many more exciting developments in the coming years. By staying up-to-date with the latest trends and techniques, you can stay ahead of the curve and unlock the full potential of machine learning in your organization.

Generative Adversarial Networks (GANs) are a type of deep learning model that has gained a lot of attention in recent years. GANs consist of two neural networks: a generator and a discriminator. The generator creates synthetic data that is similar to the real data, and the discriminator tries to distinguish between the real and synthetic data.

The goal of GANs is to train the generator to create synthetic data that is indistinguishable from real data. This data can be used for tasks such as image synthesis and data augmentation. GANs have also been used in other applications, such as generating realistic 3D models and creating deepfakes.

Transformers are a type of deep learning model that has gained a lot of attention in recent years, particularly in the field of natural language processing (NLP). The transformer architecture was introduced by Vaswani et al. (2017) and has since become a popular choice for a wide range of NLP tasks.

Traditional NLP models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), process input sequences in a linear fashion. This can lead to difficulties in modeling long-range dependencies and capturing relationships between words that are far apart in the input sequence. Transformers, on the other hand, transformers use a self-attention mechanism to process input sequences in a parallel fashion, allowing them to model long-range dependencies more effectively.

In a transformer, the input sequence is first embedded into a high-dimensional vector space. Then, multiple layers of self-attention and feedforward neural networks are applied to the input sequence in parallel. The self-attention mechanism allows the model to focus on different parts of the input sequence and learn to associate words that are far apart in the sequence. The feedforward neural networks will enable the model to learn more complex interactions between the words.

At PyImageSearch, we have crafted a three-part tutorial on Transformers (shown in Figure 2 ) to take you from the basics of attention mechanism to creating your own transformer for Neural Machine Translation.

One of the transformers’ key advantages is their ability to handle variable-length input sequences. This is particularly useful in NLP, where input sequences can vary greatly in length. In addition, transformers have been shown to outperform traditional NLP models on a wide range of tasks, including language modeling, machine translation, and text classification.

One of the most popular implementations of transformers is the BERT (Bidirectional Encoder Representations from Transformers) model, which was introduced by Google in 2018. BERT uses a transformer-based architecture to generate contextualized word embeddings, which are then used as input to downstream NLP tasks. BERT has achieved state-of-the-art performance on many NLP tasks, including sentiment analysis, question answering, and named entity recognition.

Another popular implementation of transformers is the GPT (Generative Pre-training Transformer) model, which was introduced by OpenAI in 2018. GPT uses a transformer-based architecture to generate text, and it has been used to generate realistic, human-like text in a wide range of applications, from chatbots to creative writing.

Transformers are a powerful type of deep learning model that has revolutionized the field of NLP. Their ability to handle variable-length input sequences and model long-range dependencies has made them a popular choice for a wide range of NLP tasks. As the field of NLP continues to advance, we can expect to see many more exciting developments in the area of transformer-based models.

Reinforced learning is used when the algorithm needs to learn through trial and error. In this case, the algorithm interacts with an environment and receives rewards or penalties for its actions. Reinforced learning aims to learn the optimal policy, or set of actions, that maximizes the cumulative reward over time.

Reinforced learning is often used in robotics, gaming, and autonomous vehicles. In these cases, the algorithm must learn how to navigate a complex environment and make decisions that lead to the desired outcome. By receiving feedback in rewards or penalties, the algorithm can learn from its mistakes and improve over time.

Machine learning is a powerful tool for solving a wide range of problems in many different industries. By analyzing large datasets and extracting patterns and insights, machine learning algorithms can help businesses and organizations make better decisions, improve efficiency, and reduce costs. In this blog post, we will explore some of the types of problems that can be solved with machine learning.

Classification problems are one of the most common types of problems that can be solved with machine learning. In a classification problem, the goal is to assign a label to an input based on its features. For example, a machine learning algorithm could be used to classify emails as spam or not spam or to classify images as dogs or cats.

Classification problems are often solved using supervised learning algorithms, such as decision trees, support vector machines, and neural networks. These algorithms learn to map input features to output labels by analyzing examples of labeled data.

Regression problems are another common type of problem that can be solved with machine learning. In a regression problem, the goal is to predict a continuous output value based on the input features. For example, a machine learning algorithm could be used to predict housing prices based on features such as square footage, number of bedrooms, and location.

Regression problems are also often solved using supervised learning algorithms, such as linear regression, decision trees, and neural networks. These algorithms learn to map input features to output values by analyzing examples of labeled data.

Clustering problems are a type of unsupervised learning problem. In a clustering problem, the goal is to group similar items based on their features. For example, a machine learning algorithm could be used to cluster customers based on their purchasing habits, or to group documents based on their content.

Clustering problems are often solved using unsupervised learning algorithms, such as k-means clustering, hierarchical clustering, and density-based clustering. These algorithms learn to identify patterns in the data by analyzing examples of unlabeled data.

Anomaly detection problems are another type of unsupervised learning problem. In an anomaly detection problem, the goal is to identify unusual data points that do not fit the normal patterns of the data. For example, a machine learning algorithm could be used to detect fraudulent credit card transactions based on patterns in the transaction data.

Anomaly detection problems are often solved using unsupervised learning algorithms, such as density-based clustering and autoencoders. These algorithms learn to identify patterns in the data by analyzing examples of unlabeled data.

Reinforcement learning problems are a type of machine learning problem where the goal is to learn a policy, or set of actions, that maximizes a reward signal over time. For example, a machine learning algorithm could be used to learn to play a game or navigate a robot through a maze.

Reinforcement learning problems are often solved using reinforcement learning algorithms, such as Q-learning and policy gradient methods. These algorithms learn to optimize a policy by exploring the environment and receiving feedback in the form of rewards or penalties.

Machine learning can solve a wide range of problems in many different industries. By using machine learning algorithms to analyze large datasets, businesses, and organizations can gain new insights and make better decisions, leading to improved efficiency and reduced costs.

Machine learning algorithms are the backbone of many artificial intelligence (AI) applications. Several types of algorithms are commonly used in machine learning, each with its own strengths and weaknesses. In this blog post, we will explore some of the different types of algorithms in machine learning.

Decision trees are a type of supervised learning algorithm that is commonly used for classification and regression tasks. The algorithm works by recursively splitting the data based on the values of the input features until each leaf node contains a single output value. Decision trees are easy to interpret and can handle both categorical and continuous data.

Random forests are a type of ensemble learning algorithm that combines multiple decision trees to improve the accuracy of the predictions. The algorithm works by creating a set of decision trees, each trained on a random subset of the data and features. Random forests are often used for classification and regression tasks and can handle large datasets with high-dimensional features.

Support vector machines (SVMs) are a type of supervised learning algorithm that is commonly used for classification and regression tasks. The algorithm works by finding a hyperplane that maximally separates the data into different classes or predicts a continuous output value. SVMs can handle both linear and nonlinear data and are effective for high-dimensional data with a small number of training examples.

Neural Networks: The Brain Behind AI’s Decision-Making

Neural networks are a type of supervised learning algorithm that is commonly used for classification and regression tasks. The algorithm works by simulating the function of the human brain with a network of interconnected nodes that process the input data. Neural networks are effective for high-dimensional data with complex relationships between the input features.

K-nearest neighbors (KNN) is a type of supervised learning algorithm that is commonly used for classification and regression tasks. The algorithm works by finding the k nearest neighbors to a given data point and using their labels or values to predict the output for the new data point. KNN can handle both continuous and categorical data and is effective for small datasets with low-dimensional features.

Principal component analysis is an unsupervised learning algorithm that is commonly used for dimensionality reduction. The algorithm works by finding the principal components of the data, which are the linear combinations of the input features that capture the most variance in the data. PCA can be used to reduce the dimensionality of the data, making it easier to visualize and analyze.

Clustering algorithms are unsupervised learning algorithms that group similar data points based on their features. There are several types of clustering algorithms, including k-means, hierarchical clustering, and density-based clustering. Clustering algorithms can be used to identify patterns in the data and find hidden structures.

As you can see, many different types of algorithms are used in machine learning. Each algorithm has its own strengths and weaknesses, and the choice of algorithm depends on the type of data and the specific task at hand. By using the right algorithm for the job, businesses and organizations can gain new insights and make better decisions based on the analysis of their data.

Labels are an essential component of many machine-learning algorithms. In supervised learning, labels are used to train a model to predict output values based on input features. The process of labeling data is time-consuming and requires expertise, but it is a necessary step in building effective machine-learning models.

In supervised learning, labels are attached to each data point in the training set, indicating the correct output value for that data point. For example, if the input is an image, the label might indicate whether the image contains a dog or a cat. If the input is a sentence, the label might indicate the sentiment of the sentence (positive, negative, or neutral).

Labeling data is typically done manually, either by humans or by using other machine learning algorithms. Human labeling can be time-consuming and expensive, especially for large datasets. However, it is often necessary to ensure high-quality labels, particularly for complex tasks or tasks that require human expertise.

One way to reduce the cost and time required for labeling is through semi-supervised learning. In semi-supervised learning, a small portion of the data is labeled, and the rest of the data is left unlabeled. The model is then trained on the labeled data, and the knowledge gained from this training is used to make predictions for the unlabeled data. This can be a cost-effective way to train a machine learning model, particularly for large datasets.

In addition to supervised learning, labels are also used in unsupervised learning algorithms. In clustering algorithms, for example, the goal is to group similar data points based on their features. While the data points may not have explicit labels, the clusters themselves can be used to infer labels or insights about the data.

Labels are also used in reinforcement learning, where the goal is to learn a policy that maximizes a reward signal over time. In this case, the reward signal acts as a label, indicating the correct action to take in a given situation.

You probably noticed by now that labels are an essential component of many machine learning algorithms. While the process of labeling data can be time-consuming and expensive, it is necessary to train effective machine learning models. By using labeled data, businesses and organizations can gain new insights and make better decisions based on the analysis of their data.

Analytical learning is a type of machine learning that involves using mathematical models and statistical analysis to make predictions or decisions based on data. It is one of the most common approaches to machine learning and is used in a wide range of applications, from business analytics to healthcare to autonomous vehicles.

Analytical learning is often used in supervised learning, where the goal is to predict output values based on input features. In analytical learning, a model is trained on a set of labeled data using statistical methods and mathematical models. The model then uses this knowledge to make predictions on new, unseen data.

Tabular data remains a significant and crucial format. Here are some reasons why:

• Structured and Organized: Tabular data is inherently organized in rows and columns, making it easy for humans and computers to understand and analyze.
• Legacy Systems: Many businesses and organizations still rely on databases and spreadsheets that store information in a tabular format.
• Analysis Foundation: Tabular data serves as the foundation for many machine learning algorithms, making it a vital tool for extracting insights.

Several types of analytical learning models are commonly used in machine learning. These include linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), and artificial neural networks (ANNs). Each type of model has its own strengths and weaknesses, and the choice of model depends on the specific problem and the characteristics of the data. In reality, a variety of neural network architectures can be employed to understand heterogeneous tabular data, as shown in Figure 3 .

In addition to supervised learning, analytical learning can also be used in unsupervised learning, where the goal is to identify patterns and relationships in the data. In unsupervised learning, the model is not given explicit output labels, but instead, it is used to group or cluster similar data points based on their features. Common unsupervised learning algorithms include k-means clustering, hierarchical clustering, and principal component analysis (PCA).

One key advantage of analytical learning is its ability to handle large datasets and complex relationships between the input features. By using statistical methods and mathematical models, analytical learning can extract patterns and insights from the data that may not be obvious to humans.

However, analytical learning also has limitations. For example, it may need help to handle high-dimensional data with many input features and be sensitive to outliers and noise in the data. In addition, analytical learning may not be suitable for tasks that require human expertise or judgment.

Analytical learning is a powerful tool in machine learning that can be used to make predictions or decisions based on data. It is a widely used approach that involves using mathematical models and statistical analysis to extract patterns and insights from the data. By using analytical learning, businesses and organizations can gain new insights and make better decisions based on the analysis of their data.

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In this post, we learned about various types of machine learning, such as supervised, unsupervised, and reinforcement learning, as well as insights into deep learning and its applications (e.g., GANs and transfer learning). Additionally, we also introduced different problem types, including classification, regression, clustering, and anomaly detection, and explored algorithms like decision trees, random forests, and neural networks. Now, you’re ready to dive deeper and start training your own machine-learning models to solve interesting problems. Be sure to check out our other blogs or, even better, join PyImageSearch University , where you’ll get videos, code downloads, and all the help you need to be successful in machine learning.

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AI accelerates problem-solving in complex scenarios

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While Santa Claus may have a magical sleigh and nine plucky reindeer to help him deliver presents, for companies like FedEx, the optimization problem of efficiently routing holiday packages is so complicated that they often employ specialized software to find a solution.

This software, called a mixed-integer linear programming (MILP) solver, splits a massive optimization problem into smaller pieces and uses generic algorithms to try and find the best solution. However, the solver could take hours — or even days — to arrive at a solution.

The process is so onerous that a company often must stop the software partway through, accepting a solution that is not ideal but the best that could be generated in a set amount of time.

Researchers from MIT and ETH Zurich used machine learning to speed things up.

They identified a key intermediate step in MILP solvers that has so many potential solutions it takes an enormous amount of time to unravel, which slows the entire process. The researchers employed a filtering technique to simplify this step, then used machine learning to find the optimal solution for a specific type of problem.

Their data-driven approach enables a company to use its own data to tailor a general-purpose MILP solver to the problem at hand.

This new technique sped up MILP solvers between 30 and 70 percent, without any drop in accuracy. One could use this method to obtain an optimal solution more quickly or, for especially complex problems, a better solution in a tractable amount of time.

This approach could be used wherever MILP solvers are employed, such as by ride-hailing services, electric grid operators, vaccination distributors, or any entity faced with a thorny resource-allocation problem.

“Sometimes, in a field like optimization, it is very common for folks to think of solutions as either purely machine learning or purely classical. I am a firm believer that we want to get the best of both worlds, and this is a really strong instantiation of that hybrid approach,” says senior author Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE), and a member of a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS).

Wu wrote the paper with co-lead authors Sirui Li, an IDSS graduate student, and Wenbin Ouyang, a CEE graduate student; as well as Max Paulus, a graduate student at ETH Zurich. The research will be presented at the Conference on Neural Information Processing Systems.

Tough to solve

MILP problems have an exponential number of potential solutions. For instance, say a traveling salesperson wants to find the shortest path to visit several cities and then return to their city of origin. If there are many cities which could be visited in any order, the number of potential solutions might be greater than the number of atoms in the universe.  

“These problems are called NP-hard, which means it is very unlikely there is an efficient algorithm to solve them. When the problem is big enough, we can only hope to achieve some suboptimal performance,” Wu explains.

An MILP solver employs an array of techniques and practical tricks that can achieve reasonable solutions in a tractable amount of time.

A typical solver uses a divide-and-conquer approach, first splitting the space of potential solutions into smaller pieces with a technique called branching. Then, the solver employs a technique called cutting to tighten up these smaller pieces so they can be searched faster.

Cutting uses a set of rules that tighten the search space without removing any feasible solutions. These rules are generated by a few dozen algorithms, known as separators, that have been created for different kinds of MILP problems. 

Wu and her team found that the process of identifying the ideal combination of separator algorithms to use is, in itself, a problem with an exponential number of solutions.

“Separator management is a core part of every solver, but this is an underappreciated aspect of the problem space. One of the contributions of this work is identifying the problem of separator management as a machine learning task to begin with,” she says.

Shrinking the solution space

She and her collaborators devised a filtering mechanism that reduces this separator search space from more than 130,000 potential combinations to around 20 options. This filtering mechanism draws on the principle of diminishing marginal returns, which says that the most benefit would come from a small set of algorithms, and adding additional algorithms won’t bring much extra improvement.

Then they use a machine-learning model to pick the best combination of algorithms from among the 20 remaining options.

This model is trained with a dataset specific to the user’s optimization problem, so it learns to choose algorithms that best suit the user’s particular task. Since a company like FedEx has solved routing problems many times before, using real data gleaned from past experience should lead to better solutions than starting from scratch each time.

The model’s iterative learning process, known as contextual bandits, a form of reinforcement learning, involves picking a potential solution, getting feedback on how good it was, and then trying again to find a better solution.

This data-driven approach accelerated MILP solvers between 30 and 70 percent without any drop in accuracy. Moreover, the speedup was similar when they applied it to a simpler, open-source solver and a more powerful, commercial solver.

In the future, Wu and her collaborators want to apply this approach to even more complex MILP problems, where gathering labeled data to train the model could be especially challenging. Perhaps they can train the model on a smaller dataset and then tweak it to tackle a much larger optimization problem, she says. The researchers are also interested in interpreting the learned model to better understand the effectiveness of different separator algorithms.

This research is supported, in part, by Mathworks, the National Science Foundation (NSF), the MIT Amazon Science Hub, and MIT’s Research Support Committee.

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A Guide to Solving Social Problems with Machine Learning

• Jon Kleinberg,
• Jens Ludwig,
• Sendhil Mullainathan

Predictive technology can improve public policy — if we use it right.

It’s Sunday night. You’re the deputy mayor of a big city. You sit down to watch a movie and ask Netflix for help. (“Will I like Birdemic? Ishtar? Zoolander 2?”) The Netflix recommendation algorithm predicts what movie you’d like by mining data on millions of previous movie-watchers using sophisticated machine learning tools. And then the next day you go to work and every one of your agencies will make hiring decisions with little idea of which candidates would be good workers; community college students will be largely left to their own devices to decide which courses are too hard or too easy for them; and your social service system will implement a reactive rather than preventive approach to homelessness because they don’t believe it’s possible to forecast which families will wind up on the streets.

• Jon Kleinberg is a professor of computer science at Cornell University and the coauthor of the textbooks Algorithm Design (with Éva Tardos) and Networks, Crowds, and Markets (with David Easley).
• JL Jens Ludwig is the McCormick Foundation Professor of Social Service Administration, Law and Public Policy at the University of Chicago.
• SM Sendhil Mullainathan is a professor of economics at Harvard University and the coauthor (with Eldar Shafir) of Scarcity: Why Having Too Little Means So Much.

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9 Real-World Problems that can be Solved by Machine Learning

Machine Learning has gained a lot of prominence in the recent years because of its ability to be applied across scores of industries to solve complex problems effectively and quickly. Contrary to what one might expect, Machine Learning use cases are not that difficult to come across. The most common examples of problems solved by machine learning are image tagging by Facebook and spam detection by email providers.

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AI for business can resolve incredible challenges across industry domains by working with suitable datasets. In this post, we will learn about some typical problems solved by machine learning and how they enable businesses to leverage their data accurately.

What is Machine Learning?

A sub-area of artificial intelligence, machine learning, is an IT system's ability to recognize patterns in large databases to find solutions to problems without human intervention. It is an umbrella term for various techniques and tools to help computers learn and adapt independently.

Unlike traditional programming, a manually created program that uses input data and runs on a computer to produce the output, in Machine Learning or augmented analytics, the input data and output are given to an algorithm to create a program. It leads to powerful insights that can be used to predict future outcomes.

Machine learning algorithms do all that and more, using statistics to find patterns in vast amounts of data that encompass everything from images, numbers, words, etc. If the data can be stored digitally, it can be fed into a machine-learning algorithm to solve specific problems.

Types Of Machine Learning

Today, Machine Learning algorithms are primarily trained using three essential methods. These are categorized as three types of machine learning, as discussed below –

    1. Supervised Learning

One of the most elementary types of machine learning, supervised learning, is one where data is labeled to inform the machine about the exact patterns it should look for. Although the data needs to be labeled accurately for this method to work, supervised learning is compelling and provides excellent results when used in the right circumstances.

For instance, when we press play on a Netflix show, we’re informing the Machine Learning algorithm to find similar shows based on our preference.

How it works –

• The Machine Learning algorithm here is provided with a small training dataset to work with, which is a smaller part of the bigger dataset.
• It serves to give the algorithm an idea of the problem, solution, and various data points to be dealt with.
• The training dataset here is also very similar to the final dataset in its characteristics and offers the algorithm with the labeled parameters required for the problem.
• The Machine Learning algorithm then finds relationships between the given parameters, establishing a cause and effect relationship between the variables in the dataset.

    2. Unsupervised Learning

Unsupervised learning, as the name suggests, has no data labels. The machine looks for patterns randomly. It means that there is no human labor required to make the dataset machine-readable. It allows much larger datasets to be worked on by the program. Compared to supervised learning, unsupervised Machine Learning services aren’t much popular because of lesser applications in day-to-day life. 

How does it work?

• Since unsupervised learning does not have any labels to work off, it creates hidden structures.
• Relationships between data points are then perceived by the algorithm randomly or abstractly, with absolutely no input required from human beings.
• Instead of a specific, defined, and set problem statement, unsupervised learning algorithms can adapt to the data by changing hidden structures dynamically.

    3. Reinforcement Learning

Reinforcement learning primarily describes a class of machine learning problems where an agent operates in an environment with no fixed training dataset. The agent must know how to work using feedback.

• Reinforcement learning features a machine learning algorithm that improves upon itself.
• It typically learns by trial and error to achieve a clear objective.
• In this Machine Learning algorithm, favorable outputs are reinforced or encouraged, whereas non-favorable outputs are discouraged.

Top 4 Issues with Implementing Machine Learning

While Machine learning is extensively used across industries to make data-driven decisions, its implementation observes many problems that must be addressed. Here’s a list of organizations' most common  machine learning challenges when inculcating ML in their operations.

1. Inadequate Training Data

Data plays a critical role in the training and processing of machine learning algorithms. Many data scientists attest that insufficient, inconsistent, and unclean data can considerably hamper the efficacy of ML algorithms.

2. Underfitting of Training Data

This anomaly occurs when data fails to link the input and output variables explicitly. In simpler terms, it means trying to fit in an undersized t-shirt. It indicates that data isn’t too coherent to forge a precise relationship.

3. Overfitting of Training Data

Overfitting denotes an ML model trained with enormous amounts of data that negatively affects performance. It's similar to trying an oversized jeans.

4. Delayed Implementation

ML models offer efficient results but consume a lot of time due to data overload, slow programs, and excessive requirements. Additionally, they demand timely monitoring and maintenance to deliver the best output.

9 Real-World Problems Solved by Machine Learning

Applications of Machine learning are many, including external (client-centric) applications such as product recommendation , customer service, and demand forecasts, and internally to help businesses improve products or speed up manual and time-consuming processes.

Machine learning algorithms are typically used in areas where the solution requires continuous improvement post-deployment. Adaptable machine learning solutions are incredibly dynamic and are adopted by companies across verticals.

Here we are discussing nine Machine Learning use cases –

    1. Identifying Spam

Spam identification is one of the most basic applications of machine learning. Most of our email inboxes also have an unsolicited, bulk, or spam inbox, where our email provider automatically filters unwanted spam emails. 

But how do they know that the email is spam?

They use a trained Machine Learning model to identify all the spam emails based on common characteristics such as the email, subject, and sender content. 

If you look at your email inbox carefully, you will realize that it is not very hard to pick out spam emails because they look very different from real emails. Machine learning techniques used nowadays can automatically filter these spam emails in a very successful way. 

Spam detection is one of the best and most common problems solved by Machine Learning. Neural networks employ content-based filtering to classify unwanted emails as spam. These neural networks are quite similar to the brain, with the ability to identify spam emails and messages.

    2. Making Product Recommendations

Recommender systems are one of the most characteristic and ubiquitous machine learning use cases in day-to-day life. These systems are used everywhere by search engines, e-commerce websites (Amazon), entertainment platforms (Google Play, Netflix), and multiple web & mobile apps.

Prominent online retailers like Amazon and eBay often show a list of recommended products individually for each of their consumers. These recommendations are typically based on behavioral data and parameters such as previous purchases, item views, page views, clicks, form fill-ins, purchases, item details (price, category), and contextual data (location, language, device), and browsing history.  

These recommender systems allow businesses to drive more traffic, increase customer engagement, reduce churn rate, deliver relevant content and boost profits. All such recommended products are based on a machine learning model’s analysis of customer’s behavioral data. It is an excellent way for online retailers to offer extra value and enjoy various upselling opportunities using machine learning.

    3. Customer Segmentation

Customer segmentation, churn prediction and customer lifetime value (LTV) prediction are the main challenges faced by any marketer. Businesses have a huge amount of marketing relevant data from various sources such as email campaigns, website visitors and lead data.

Using data mining and machine learning , an accurate prediction for individual marketing offers and incentives can be achieved. Using ML, savvy marketers can eliminate guesswork involved in data-driven marketing.

For example, given the pattern of behavior by a user during a trial period and the past behaviors of all users, identifying chances of conversion to paid version can be predicted. A model of this decision problem would allow a program to trigger customer interventions to persuade the customer to convert early or better engage in the trial.

    4. Image & Video Recognition

Advances in deep learning (a subset of machine learning) have stimulated rapid progress in image & video recognition techniques over the past few years. They are used for multiple areas, including object detection, face recognition, text detection, visual search, logo and landmark detection, and image composition.

Since machines are good at processing images, Machine Learning algorithms can train Deep Learning frameworks to recognize and classify images in the dataset with much more accuracy than humans. 

Similar to image recognition , companies such as Shutterstock , eBay , Salesforce , Amazon , and Facebook use Machine Learning for video recognition where videos are broken down frame by frame and classified as individual digital images.

    5. Fraudulent Transactions

Fraudulent banking transactions are quite a common occurrence today. However, it is not feasible (in terms of cost involved and efficiency) to investigate every transaction for fraud, translating to a poor customer service experience.

Machine Learning in finance can automatically build super-accurate predictive maintenance models to identify and prioritize all kinds of possible fraudulent activities. Businesses can then create a data-based queue and investigate the high priority incidents.

It allows you to deploy resources in an area where you will see the greatest return on your investigative investment. Further, it also helps you optimize customer satisfaction by protecting their accounts and not challenging valid transactions. Such fraud detection using machine learning can help banks and financial organizations save money on disputes/chargebacks as one can train Machine Learning models to flag transactions that appear fraudulent based on specific characteristics.

    6. Demand Forecasting

The concept of demand forecasting is used in multiple industries, from retail and e-commerce to manufacturing and transportation. It feeds historical data to Machine Learning algorithms and models to predict the number of products, services, power, and more.

It allows businesses to efficiently collect and process data from the entire supply chain, reducing overheads and increasing efficiency.

ML-powered demand forecasting is very accurate, rapid, and transparent. Businesses can generate meaningful insights from a constant stream of supply/demand data and adapt to changes accordingly. 

    7. Virtual Personal Assistant

From Alexa and Google Assistant to Cortana and Siri, we have multiple virtual personal assistants to find accurate information using our voice instruction, such as calling someone, opening an email, scheduling an appointment, and more.

These virtual assistants use Machine Learning algorithms for recording our voice instructions, sending them over the server to a cloud, followed by decoding them using Machine Learning algorithms and acting accordingly.

    8. Sentiment Analysis

Sentiment analysis is one of the beneficial and real-time machine learning applications that help determine the emotion or opinion of the speaker or the writer. 

For instance, if you’ve written a review, email, or any other form of a document, a sentiment analyzer will be able to assess the actual thought and tone of the text. This sentiment analysis application can be used to analyze decision-making applications, review-based websites, and more.

    9. Customer Service Automation

Managing an increasing number of online customer interactions has become a pain point for most businesses. It is because they simply don’t have the customer support staff available to deal with the sheer number of inquiries they receive daily.

Machine learning algorithms have made it possible and super easy for chatbots and other similar automated systems to fill this gap. This application of machine learning enables companies to automate routine and low priority tasks, freeing up their employees to manage more high-level customer service tasks. 

Further, Machine Learning technology can access the data, interpret behaviors and recognize the patterns easily. This could also be used for customer support systems that can work identical to a real human being and solve all of the customers’ unique queries. The Machine Learning models behind these voice assistants are trained on human languages and variations in the human voice because it has to efficiently translate the voice to words and then make an on-topic and intelligent response.

If implemented the right way, problems solved by machine learning can streamline the entire process of customer issue resolution and offer much-needed assistance along with enhanced customer satisfaction.

Wrapping Up

As advancements in machine learning evolve, the range of use cases and applications of machine learning too will expand. To effectively navigate the business issues in this new decade, it’s worth keeping an eye on how machine learning applications can be deployed across business domains to reduce costs, improve efficiency and deliver better user experiences.

However, to implement machine learning accurately in your organization, it is imperative to have a trustworthy partner with deep-domain expertise. At Maruti Techlabs, we offer advanced machine learning services that involve understanding the complexity of varied business issues, identifying the existing gaps, and offering efficient and effective tech solutions to manage these challenges.

If you wish to learn more about how machine learning solutions can increase productivity and automate business processes for your business, get in touch with us .

Pinakin is the VP of Data Science and Technology at Maruti Techlabs. With about two decades of experience leading diverse teams and projects, his technological competence is unmatched.

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The Intersection of Math and AI: A New Era in Problem-Solving

By Whitney Clavin, California Institute of Technology (Caltech) December 11, 2023

The Mathematics and Machine Learning 2023 conference at Caltech highlights the growing integration of machine learning in mathematics, offering new solutions to complex problems and advancing algorithm development.

Conference is exploring burgeoning connections between the two fields.

Traditionally, mathematicians jot down their formulas using paper and pencil, seeking out what they call pure and elegant solutions. In the 1970s, they hesitantly began turning to computers to assist with some of their problems. Decades later, computers are often used to crack the hardest math puzzles. Now, in a similar vein, some mathematicians are turning to machine learning tools to aid in their numerical pursuits.

Embracing Machine Learning in Mathematics

“Mathematicians are beginning to embrace machine learning,” says Sergei Gukov, the John D. MacArthur Professor of Theoretical Physics and Mathematics at Caltech, who put together the Mathematics and Machine Learning 2023 conference, which is taking place at Caltech December 10–13.

“There are some mathematicians who may still be skeptical about using the tools,” Gukov says. “The tools are mischievous and not as pure as using paper and pencil, but they work.”

Machine Learning: A New Era in Mathematical Problem Solving

Machine learning is a subfield of AI, or artificial intelligence, in which a computer program is trained on large datasets and learns to find new patterns and make predictions. The conference, the first put on by the new Richard N. Merkin Center for Pure and Applied Mathematics, will help bridge the gap between developers of machine learning tools (the data scientists) and the mathematicians. The goal is to discuss ways in which the two fields can complement each other.

Mathematics and Machine Learning: A Two-Way Street

“It’s a two-way street,” says Gukov, who is the director of the new Merkin Center, which was established by Caltech Trustee Richard Merkin.

“Mathematicians can help come up with clever new algorithms for machine learning tools like the ones used in generative AI programs like ChatGPT, while machine learning can help us crack difficult math problems.”

Yi Ni, a professor of mathematics at Caltech, plans to attend the conference, though he says he does not use machine learning in his own research, which involves the field of topology and, specifically, the study of mathematical knots in lower dimensions. “Some mathematicians are more familiar with these advanced tools than others,” Ni says. “You need to know somebody who is an expert in machine learning and willing to help. Ultimately, I think AI for math will become a subfield of math.”

The Riemann Hypothesis and Machine Learning

One tough problem that may unravel with the help of machine learning, according to Gukov, is known as the Riemann hypothesis. Named after the 19th-century mathematician Bernhard Riemann, this problem is one of seven Millennium Problems selected by the Clay Mathematics Institute; a $1 million prize will be awarded for the solution to each problem.

The Riemann hypothesis centers around a formula known as the Riemann zeta function, which packages information about prime numbers. If proved true, the hypothesis would provide a new understanding of how prime numbers are distributed. Machine learning tools could help crack the problem by providing a new way to run through more possible iterations of the problem.

Mathematicians and Machine Learning: A Synergistic Relationship

“Machine learning tools are very good at recognizing patterns and analyzing very complex problems,” Gukov says.

Ni agrees that machine learning can serve as a helpful assistant. “Machine learning solutions may not be as beautiful, but they can find new connections,” he says. “But you still need a mathematician to turn the questions into something computers can solve.”

Knot Theory and Machine Learning

Gukov has used machine learning himself to untangle problems in knot theory. Knot theory is the study of abstract knots, which are similar to the knots you might find on a shoestring, but the ends of the strings are closed into loops. These mathematical knots can be entwined in various ways, and mathematicians like Gukov want to understand their structures and how they relate to each other. The work has relationships to other fields of mathematics such as representation theory and quantum algebra, and even quantum physics.

In particular, Gukov and his colleagues are working to solve what is called the smooth Poincaré conjecture in four dimensions. The original Poincaré conjecture, which is also a Millennium Problem, was proposed by mathematician Henri Poincaré early in the 20th century. It was ultimately solved from 2002 to 2003 by Grigori Perelman (who famously turned down his prize of $1 million). The problem involves comparing spheres to certain types of manifolds that look like spheres; manifolds are shapes that are projections of higher-dimensional objects onto lower dimensions. Gukov says the problem is like asking, “Are objects that look like spheres really spheres?”

The four-dimensional smooth Poincaré conjecture holds that, in four dimensions, all manifolds that look like spheres are indeed actually spheres. In an attempt to solve this conjecture, Gukov and his team develop a machine learning approach to evaluate so-called ribbon knots.

“Our brain cannot handle four dimensions, so we package shapes into knots,” Gukov says. “A ribbon is where the string in a knot pierces through a different part of the string in three dimensions but doesn’t pierce through anything in four dimensions. Machine learning lets us analyze the ‘ribboness’ of knots, a yes-or-no property of knots that has applications to the smooth Poincaré conjecture.”

“This is where machine learning comes to the rescue,” writes Gukov and his team in a preprint paper titled “ Searching for Ribbons with Machine Learning .” “It has the ability to quickly search through many potential solutions and, more importantly, to improve the search based on the successful ‘games’ it plays. We use the word ‘games’ since the same types of algorithms and architectures can be employed to play complex board games, such as Go or chess, where the goals and winning strategies are similar to those in math problems.”

The Interplay of Mathematics and Machine Learning Algorithms

On the flip side, math can help in developing machine learning algorithms, Gukov explains. A mathematical mindset, he says, can bring fresh ideas to the development of the algorithms behind AI tools. He cites Peter Shor as an example of a mathematician who brought insight to computer science problems. Shor, who graduated from Caltech with a bachelor’s degree in mathematics in 1981, famously came up with what is known as Shor’s algorithm, a set of rules that could allow quantum computers of the future to factor integers faster than typical computers, thereby breaking digital encryption codes.

Today’s machine learning algorithms are trained on large sets of data. They churn through mountains of data on language, images, and more to recognize patterns and come up with new connections. However, data scientists don’t always know how the programs reach their conclusions. The inner workings are hidden in a so-called “black box.” A mathematical approach to developing the algorithms would reveal what’s happening “under the hood,” as Gukov says, leading to a deeper understanding of how the algorithms work and thus can be improved.

“Math,” says Gukov, “is fertile ground for new ideas.”

The conference will take place at the Merkin Center on the eighth floor of Caltech Hall.

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Problem Solving the Easy Way with Machine Learning: An Approach

New to machine learning?

If you have shopped online, then knowingly or unknowingly you have contributed to machine learning. Yes, we are talking about the ‘customers who bought this item also bought’ recommendations on shopping sites. And no, those are not arbitrary.

Source: Starecat.com

In the most elementary definition, machine learning means “the field of study that gives computers the ability to learn without being explicitly programmed.” This ‘learning-ability’ trait is of profound importance as over the centuries this sole trait ‘of being able to learn, retain, improve and transfer knowledge’ has differentiated human beings from other species that more or less start at the same point basically reinventing the wheel again and again.

To understand machine learning let’s consider a simple problem set:

Case 1: We know these are fruits and all we need to do is to classify them.

Case 2: We know these are fruits but we want to know how fresh they are.

Case 3: We don’t know whether these are fruits or something else and we want the algorithm to decide it for us.

Case 1: Supervised Learning - a simple ‘Train me’ scenario in which limited data is fed to the computer for a simple Yes/No classification.

The process:

Labeled data is fed to the computer→ the algorithm understands labeled data and segregates it into patterns and associations → the algorithm identifies a label for the new data

Source: Research Gate

Supervised learning approaches the above problem in three steps:

Step 1: Prepare a training data marking physical characteristics of fruits.

Step 2: Feed in the decision variable. The algorithm will decide the name of the fruits based on the training data.

Step 3: For example, if the fruit is big, red and round with depression on top then it will go to the apple group. Common Algorithms used for supervised learning: Linear regression, logistic regression, support vector machines, decision trees, naive Bayes, k-nearest neighbor algorithm, linear discriminant analysis, Neural Networks (Multilayer perceptron), similarity learning

Case 2: Reinforcement Learning - ‘I can learn on my own’ scenario in which unlimited data is fed for learning and continuous improvement.

The process: Step 1: Create a training data of oranges (Fresh, Not fresh, Rotten) Step 2: Feed data into the algorithm to decide the age.

Common Algorithms for reinforcement learning: Clustering (hierarchical clustering, k-means, mixture models), Anomaly detection(Local Outlier Factor), Neural Networks(Autoencoders, Deep Belief Nets, Hebbian Learning, Self-organizing map), latent variable models (Expectation–maximization algorithm (EM), Method of moments).

Case 3: Unsupervised Learning- ‘I’ll find what to learn and learn it myself’ scenario in which unlimited data is fed to the computer for making sense and further learning.

Human communication has always been a complex synthesis of verbal and non-verbal clues. Among other factors conveyed, the meaning of the said words also depends upon the experiences of the listener and, the tone and the body language of the speaker. But something that has played such a pivotal role in the evolution of the most evolved species in time ought to be intricate which makes it difficult to replicate too. In short, an AI designed to communicate must be able to work at the level of human intelligence. Previously thought impossible, this elevation now seems quite probable with the help of machine learning and natural language processing (NLP).

A commonplace in the online world, scripted chatbots are no strangers to us. Designed for specific purposes and audience, these chatbots use predefined scripts to perform actions and answer a certain set of questions. But these were just an extension of written FAQs. Enter Intelligent chatbots and things change— Forever. Intelligent chatbots learn with every interaction with their clients or in other words, they work on NLP.

Begin by classifying fruits based on their physical traits.

Color - Yellow

Grapes or papaya.

Shape - Round or Oval, bunch shape cylindrical or Oval

Round and bunch-shaped: grapes

Oval: grapes and papaya.

Size - (Big/small)

Small: Grapes

Big: Papaya

Source: Medium

Common algorithms for unsupervised learning: Clustering, Anomaly detection, Neural Networks, Expectation–maximization algorithm (EM), Method of moments, Blind signal separation

Supervised machine learning is used:

• When results are predictable.
• Where inputs and outputs are known and we need a simple classification of data.

Reinforcement machine learning is used:

• When results are known and continuous refinement is needed.

Un-supervised machine learning is used:

• When results are unknown.

Machine learning has found applications in a plethora of business purposes. We list the most common ones here:

Supervised learning:

• Google’s PayPerClick campaign
• Spam marking systems
• Facebook’s face recognition

Reinforcement learning:

• Inventory management robots
• Advertising
• Content optimization

Unsupervised learning:

• Trending news
• Movie recommendations
• Identifying customers for a loyalty program

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Computer Science > Machine Learning

Title: solving machine learning problems.

Abstract: Can a machine learn Machine Learning? This work trains a machine learning model to solve machine learning problems from a University undergraduate level course. We generate a new training set of questions and answers consisting of course exercises, homework, and quiz questions from MIT's 6.036 Introduction to Machine Learning course and train a machine learning model to answer these questions. Our system demonstrates an overall accuracy of 96% for open-response questions and 97% for multiple-choice questions, compared with MIT students' average of 93%, achieving grade A performance in the course, all in real-time. Questions cover all 12 topics taught in the course, excluding coding questions or questions with images. Topics include: (i) basic machine learning principles; (ii) perceptrons; (iii) feature extraction and selection; (iv) logistic regression; (v) regression; (vi) neural networks; (vii) advanced neural networks; (viii) convolutional neural networks; (ix) recurrent neural networks; (x) state machines and MDPs; (xi) reinforcement learning; and (xii) decision trees. Our system uses Transformer models within an encoder-decoder architecture with graph and tree representations. An important aspect of our approach is a data-augmentation scheme for generating new example problems. We also train a machine learning model to generate problem hints. Thus, our system automatically generates new questions across topics, answers both open-response questions and multiple-choice questions, classifies problems, and generates problem hints, pushing the envelope of AI for STEM education.

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• 10 Real World Problems That Machine Learning Can Solve

Machine Learning is one of the modern-day technologies, which was also just an idea a few years back. None of us ever believed the fact that our vision can be evolved to a point where it starts to feel like science fiction. But we have narrowed the possibilities as we are getting closer to automation by building intelligent machines capable of performing tasks without any human touch or intelligence.

It all started in 1943 when the brilliant mathematician Alan Turing created “The Bombe,” a machine that was cracking a staggering total of 80,000 Enigma messages each month. Not only did it help Allied Forces win World War II, but it also asked a simple question “Can machines figure out ideas on their own?”

We strongly believe that introduction of Artificial Intelligence and its sub-parts like Machine Learning is the answer to that question. Today we are not talking about one of the trending advances in technology which is hot today and forgotten tomorrow; we are discussing Machine Learning which is here to stay and make lives easier on both ends. You might have no idea about “Machine Learning,” but after reading this blog, you will know how it shapes and streamlines the way we work, live, and communicate, in short, “Our Future.” 

What is Machine Learning?

Machine Learning is a sub-array of artificial intelligence where computer algorithms independently learn from data and information without any human intervention. It’s a practice of using built-in algorithms to analyze data and further learn from it. 

The objective of machine learning is to adapt to new data independently and make decisions and suggestions based on thousands of calculations and analyses. As it is a sub-part of artificial intelligence, the process is accomplished by infusing deep learning applications and AI machines from their fed data. 

How Do Problem-Solving And Machine Learning Co-Relates?

The basic definition of problem-solving is to find the most accurate process of finding solutions to complex problems. When we look at the ML, then it is pretty vast and is expanding rapidly. How do problem-solving and machine learning correlate? 

In 1977, Tom Mitchell shared a “well-conditioned” definition to the ML, which we believe is the perfect representation of today’s market scenario and how we can use ML to make things easier for businesses from all around the globe. He stated, “An ML is a computer program which is said to learn from experience E to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”

So, suppose you want your program to predict something, for example, identifying a person from a group of individuals using their interaction methods (task T). In that case, you can feed every individual’s way of interaction data (experience E) to a machine learning algorithm. It will successfully learn to PREDICT the individual pattern through their respective way of interaction (performance measure P). It means you are always one step ahead as you already know what they might say or do. 

With the inclusion of machine learning in your business, you can use the previously gathered data from your industry to develop better ways than the existing solutions using Machine Learning. The main objective of ML is never to make “perfect” speculations because ML trades in domains. Its main goal is to come up with good enough estimates so these predictions can be helpful in different ways. 

Who’s Using It?

The principal element of machine learning is data. All the major industries that work with large amounts of data have already acknowledged the worth of machine learning technology. With endless data, you have endless possibilities. It opens gates to many practical ways that can help scale your business to further domains. By gathering valuable insights from this data, organizations are finding a more efficient way to work and gaining an edge over their competitors.

Some of the top industries in the world that are currently using Machine Learning are: 

• Financial Services

Banks and other financial institutions in Singapore are using ML for two key purposes: preventing fraud and identifying essential insights in data.

• Transportation

ML has already become a crucial prospect to delivery companies and other transportation enterprises. They use it to analyze data to identify patterns and even trends, making routes more efficient and even predicting potential problems to increase profitability.  

• Health and Care

Health Care is also reaping the benefit of ML as it is helping medical experts analyze data to identify patterns to offer better diagnoses and treatment to the patients. 

Ecommerce has already become part of our daily lives. Retail businesses are using ML to recommend items that users might LIKE by analyzing their previous purchases.

• Oil & Gas

Machine learning in the Oil and Gas industry is immense and will expand to various factors very soon. It is currently being used to detect new energy sources, analyze minerals in the ground, and even anticipate any kind of refinery sensor failures. 

10 Real-World Problems that Machine Learning can solve           

1. recommending products after collecting previous data.

Recommendation systems are one of the most common machine learning use cases in day-to-day life. These systems are used mainly by search engines like Google and Bing and the top eCommerce platforms like Amazon and eBay.

The ML integrated systems show a list of recommended products individually for each of their consumers. These suggestions are based on data like previous purchases, wish lists, searches, clicks, inquiries, and browsing history. This data is fed to a comprehensive ML algorithm to strike the user at the right moment and enhance their customer engagement. 

2. Works as the Best Image and Video Recognition Tool

If you have come across features like face recognition, text detection, object detection, and landmark detection, it is because of the integration of deep learning in machine learning. When ML algorithms are trained with innovative deep learning frameworks, they can quickly identify and classify objects and make things easier for a non-native person.

MNL can also be used to determine handwritten text by segmenting a piece of writing into smaller images, each containing a single character. 

3. Your Virtual Assistant

A virtual assistant, which is also very common as an AI assistant, is an application program that comprehends natural language voice commands and finishes the tasks for the users like searching the web, booking an appointment, etc. If you have also asked Google Assistant in your android phones to wake you up at 5 AM or asked Siri on your iPhones for directions to the nearest restaurant, then ML has also made your life easier.

Some principal personal assistants or smart assistants available in the market are Siri, Google Assistants, Alexa, Echo, and Google mini. These assistants can help you look for information by voice commands or answer your questions by searching your query on the web. 

4. Ingenious Gaming Using ML

With the advancement in technologies, we can improve the graphics of the games and give them a mind at the same time. Lately, if you have been facing difficulties beating the bot in a chess game, then ML might take it over. Today’s games not only simply analyze your moves but are also learning how to play the game better than you by practicing numerous times. Now using your mind against such an intelligent system will surely give you brains and make you smarter at the same time. 

5. Devising Superior Health Care Methods

Even hospitals are utilizing machine learning to cure and treat patients. Thanks to our wearable devices, doctors can get accurate data on our health from anywhere in the world and suggest an aid if they find something helpful. The integration of ML in some essential tools can quickly provide real-time insights and combine with the explosion of computing power. 

It can help doctors to diagnose critically faster and more accurately. Not only this, AI is assisting in the development of new medications and treatments, predicting harmful reactions at the early stages, and working towards finding a way to lower the costs of healthcare for providers and patients. 

6. Protecting Environment in the Most Impactful Way

Aforesaid, possibilities are endless with ML, and it’s just the beginning. Recently IBM’s Green Horizon Project was acknowledged by experts worldwide as it accurately predicted weather and pollution forecasts. We can use it to save and predict natural forecasts with the expertise of the professionals from Singapore by our side. It is helping city planners to run every kind of scenario just by feeding previous data to their ML algorithm to find ways for minimum environmental impact. 

7. Real-Time Dynamic Pricing

You might have already encountered this scenario while booking a flight ticket to travel on Christmas or booking a cab at peak hours. You will notice a big gap between the regular pricing and pricing at that particular time. So, in these scenarios, the ML and data analysis techniques are helping businesses to get to know more about their users. It answers two critical questions. 

First, how are customers reacting to surge prices? And second, whether they are looking for customers because of surge pricing? The integration of AI and ML helps the businesses and the users as it helps determine when customers are looking for the best promotional and discounted prices. 

8. Innovations in the Finance Sector Including Stock Market

The functioning of the finance sector is about to change in the upcoming years completely. Thanks to technologies like mobile app development and machine learning, the stock market is at its all-time high. 

Thanks to AI, deep learning, and machine learning, it has become easy for users to predict the market price by feeding it the previous data. It will allow traders to make better and steady decisions which means less financial loss. Not only this, the machine learning-based anomaly detection models can easily monitor your every transaction request and alert you of any kind of suspicious activity.

9. Commute Predictions Using Machine Learning

Almost everyone uses GPS services while driving. A programmed GPS helps us in finding the proper navigation to our destination. But it’s the integration of ML and its features like congestion analysis GPS that helps us by telling us the path to avoid traffic and reach our destination on time. It saves the data like our location and velocities of the vehicles on the same path to determine the traffic and let us know whether it would be the right path to follow. 

10. Online Video OTT Streaming Applications 

The pioneers of online video streaming services are Netflix and Amazon Prime; both of them combined have killed the traditional way of watching television. But how were they able to keep the customer engaged on their platforms? First by offering impressive content, secondly by getting personalized with the users. 

They were able to capture mass audiences using machine learning. At the right time, they integrated ML in their program and fed it the user’s data like day and time they watch content, type of content they like to watch, browsing pattern, whether they like to watch trailers before they watch a movie or a show, etc. They are using practical machine learning frameworks to engage their audience by providing quality streaming service right to their homes. 

The possibilities with machine learning are limitless. All you need to do is find a comprehensive way to use it in your particular business domain to improve your services. Sometimes it can be indispensable to understand the problem at hand, as you can’t use any ML algorithm for your business needs.

Every problem is different from the previous one in machine learning. This means that you can’t just feed some data to a machine learning algorithm with a neural network and pray for the results.

Every situation demands a different approach and that is why it is crucial to consider looking for professional Machine Learning experts.

People who must have enough experience in the field and can work with an open mind to first understand your business requirements. And then come up with the incredible machine learning algorithm that benefits your business immensely without wasting anyone’s time. 

At ICore Singapore, you get all-inclusive Artificial Intelligence and Machine Learning Development services, redefining the way your business operates.

With the right mix of AI/ML development teams, you can trust us for high-quality solutions that cover all your needs.

Our Writing team believes you had a great time reading this piece. If this helped you in any possible way, a pat on our back would be great!

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Machine Learning

The broad goal of machine learning is to automate the decision-making process, so that computer-automated predictions can make a task more efficient, accurate, or cost-effective than it would be using only human decision making.

Carnegie Mellon is widely regarded as one of the world’s leading centers for machine learning research, and the scope of our machine learning research is broad. Our current research addresses learning in games, where there are multiple learners with different interests; semi-supervised learning; astrostatistics; intrusion detection; and structured prediction.

Our is distinguished by its serious focus on applications and real systems. A notable example from machine learning is research that has led a system for early detection of disease outbreaks. Carnegie Mellon has also received ongoing recognition from its Robotic soccer research program, which provides a rich environment for machine learning that “improves with experience,” involving problem solving in complex domains with multiple agents, dynamic environments, the need for learning from feed-back, real-time planning, and many other artificial intelligence issues.

Faculty Working in this Area