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Introduction to Machine learning
- by Refresh Science
- July 17, 2020 January 22, 2023
Machine learning is an application of artificial intelligence that involves algorithms and data that automatically analyse and make decision by itself without human intervention. It describes how computer perform tasks on their own by previous experiences. Therefore we can say in machine language artificial intelligence is generated on the basis of experience.
The difference between normal computer software and machine learning is that a human developer hasn’t given codes that instructs the system how to react to situation, instead it is being trained by a large number of data.
Uses of Machine Learning
Some of the machine learning algorithms are:
- Neural Networks
- Random Forests
- Decision trees
- Genetic algorithm
- Radial basis function
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Types of Machine Learning
There are three types of machine learning
Supervised learning
- Unsupervised learning
- Reinforcement learning
Supervised learning is a technique where the program is given labelled input data and the expected output data. It gets the data from training data containing sets of examples. They generate two kinds of results :
Classification: They notify the class of the data it is presented with.
Regression: they expect the product to produce a numerical value.
UNSUPERVISED LEARNING
This type of algorithm consists of input data without labelled response. There will not be any pre existing labels and human intervention is also less. It is mostly used in exploratory analysis as it can automatically identify the structure in data.
REINFORCEMENT LEARNING
This model is used in making a sequence of decisions. It is an learning by interacting with the environment. It is based on the observation that intelligent agents tend to repeat the action that are rewarded for and refrain from action that are punished for. It can be said that it is an trail and error method in finding the best outcome based on experience.
Machine Learning Uses:
- Traffic prediction
- Virtual Personal Assistant
- Speech recognition
- Email spam and malware filtering
- Bioinformatics
- Natural language processing
Real Time Examples for Machine Learning
Traffic prediction:.
By using GPS navigation service out location are saved at the central server for managing traffic. Based on the number of gps tracked at the location traffic at the particular Street is identified.
Virtual Personal Assistant:
Smart Speakers, Smartphones and apps like google allo.
Online Transportation:
In apps like uber the available vehicle near our area, the estimated cost and distance of the travel are computed using this technique.
Social Media Services:
In app like Facebook personalizing our news feed, people you may know are done using Machine learning.
Email spam filtering:
There are number of approaches clients use. These filters are continuously updated and powered by machine learning.
Product Recommendation:
In online shopping while we search for a product all its relavant products are displayed in our screen . It is based on the technique of machine learning.
Online Fraud detection:
Tracking monetary frauds online by making cyber space a secure place is an example of machine learning.
Best Programming Languages for Machine Learning:
Some of the best and most commonly used machine learning programs are
- JavaScript,
Machine Learning vs Artificial Intelligence
Difference Between Machine Learning And Artificial Intelligence
Artificial Intelligence is a concept of creating intelligent machines that stimulates human behaviour whereas Machine learning is a subset of Artificial intelligence that allows machine to learn from data without being programmed.
Advantages of ML
- Fast, Accurate, Efficient.
- Automation of most applications.
- Wide range of real life applications.
- Enhanced cyber security and spam detection.
- No human Intervention is needed.
- Handling multi dimensional data.
Disadvantages of ML
- It is very difficult to identify and rectify the errors.
- Data Acquisition.
- Interpretation of results Requires more time and space.
Machine Learning , Tom Mitchell, McGraw-Hill.
Slides for instructors:.
Slides are available in both postscript, and in latex source. If you take the latex, be sure to also take the accomanying style files, postscript figures, etc.
- Ch 1. Introduction. ( postscript 3.8Meg), ( gzipped postscript 317k) ( pdf ) ( latex source )
- Ch 2. Concept Learning. ( postscript 347k), ( gzipped postscript 100k) ( pdf ) ( latex source )
- Ch 3. Decision Tree Learning. ( postscript 530k), ( gzipped postscript 143k) ( pdf ) ( latex source )
- Ch 4. Artificial Neural Networks. ( postscript 1.83Meg), ( gzipped postscript 329k) ( pdf ) ( latex source )
- Ch 5. Evaluating Hypotheses. ( postscript 212k), ( gzipped postscript 67k) ( pdf ) ( latex source )
- Ch 6. Bayesian Learning. ( postscript 261k), ( gzipped postscript 81k) ( pdf ) ( latex source ) see also slides on learning Bayesian networks by Friedman and Goldszmidt.
- Ch 7. Computational Learning Theory. ( postscript 160k), ( gzipped postscript 50k) ( pdf ) ( latex source )
- Ch 8. Instance Based Learning. ( postscript 138k), ( gzipped postscript 39k) ( pdf ) ( latex source )
- Ch 9. Genetic Algorithms. ( postscript 245k), ( gzipped postscript 72k) ( pdf ) ( latex source )
- Ch 10. Learning Sets of Rules. ( postscript 185k), ( gzipped postscript 57k) ( pdf ) ( latex source )
- Ch 11. Analytical Learning. ( postscript 261k) ( pdf ) ( latex source )
- Ch 12. Combining Inductive and Analytical Learning. ( postscript 419k), ( gzipped postscript 103k) ( pdf ) ( latex source )
- Ch 13. Reinforcment Learning. ( postscript 172k), ( gzipped postscript 40k) ( pdf ) ( latex source )
Additional homework and exam questions:
Additional tutorial materials:, support vector machines:.
- Tutorial information on Support vector machines
- Freeware implementation : SVM Light by Thorsten Joachims.
- K .-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf. An introduction to kernel-based learning algorithms . IEEE Neural Networks , 12(2):181-201, May 2001. ( PDF )
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Machine Learning Infographics
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Machine learning is the future of science! It allows computers to identify trends, patterns, manage data… and all that while improving themselves on their own! Since it is so profitable, machine learning and data science studies are becoming more and more common on universities and employers are always hiring. Explain how computers went from “Hello world!” to being almost independent with these infographics: they are full of editable resources so that you can explain neural networks, algorithms, the differences between supervised, unsupervised and reinforcement learning… Science won’t be complicated if you use visual elements like these to explain it!
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It contains more than 115 slides, covering total Machine Learning which takes minimum 3 hours. Me with my juniors prepared those slides on our own and presented those slides in Computational Intillegence Lab, Department of AeroSpace Engineering, IISc Bengalore. The content of those slides are extracted from various Learning sources like Coursera, Udemy, GeeksforGeeks.
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Introducing our Process Automation And Machine Learning Model set of slides. The topics discussed in these slides are Process Automation And Machine Learning Model. This is an immediately available PowerPoint presentation that can be conveniently customized. Download it and convince your audience.
This template covers the benefits of AI for the Sales and Marketing team. AI and Machine Learning benefits in the area of lead quality, engagement, and better understanding of buyer intent. Deliver an outstanding presentation on the topic using this How Ai And Machine Learning Can Revolutionize Servicenow Performance Analytics. Dispense information and present a thorough explanation of AI And Machine Learning, Revolutionize Sales And Marketing using the slides given. This template can be altered and personalized to fit your needs. It is also available for immediate download. So grab it now.
Presenting virtuous cycle for machine learning. This is a virtuous cycle for machine learning. This is a five stage process. The stages in this process are virtuous cycle, virtuous circle, chains of events.
Enthrall your audience with this Generative Adversarial Networks GANs Powerpoint Presentation Slides. Increase your presentation threshold by deploying this well-crafted template. It acts as a great communication tool due to its well-researched content. It also contains stylized icons, graphics, visuals etc, which make it an immediate attention-grabber. Comprising sixty four slides, this complete deck is all you need to get noticed. All the slides and their content can be altered to suit your unique business setting. Not only that, other components and graphics can also be modified to add personal touches to this prefabricated set.
Presenting this set of slides with name Machine Learning Life Cycle For Model Data. This is a five stage process. The stages in this process are Define Project Objectives, Acquire And Explore Data, Model Data, Interpret And Communicate, Implement, Document And Maintain. This is a completely editable PowerPoint presentation and is available for immediate download. Download now and impress your audience.
Presenting this set of slides with name Machine Learning Ppt Powerpoint Presentation Inspiration Infographics. This is a four stage process. The stages in this process are Machine Learning, Predictive Models, Complicated Process, Explicit Programming, Training. This is a completely editable PowerPoint presentation and is available for immediate download. Download now and impress your audience.
Presenting our Machine Learning Recognition Classification Prediction Extraction PPT Template. The slides are completely formattable according to your unique needs. The template is also available in widescreen dimensions to allow for better projection and flexibility in presentation. The eight sections are well proportioned and have ideal space for adding information under each heading. This slide template is usable on both MS PowerPoint as well as on Google Slides.
Presenting our set of slides with name Machine Learning Process With Modeling And Testing. This exhibits information on six stages of the process. This is an easy to edit and innovatively designed PowerPoint template. So download immediately and highlight information on Evaluate Model With Multiple Metrics, Optimize Runtime And Memory, Result Analysis, Detailed Project Execution Plan, Data Exploration.
This slide covers various benefits of adopting AI technologies in the business. It includes both tangible benefits as well as intangible benefits.Introducing Benefits Of Adopting Ai Technologies Artificial Intelligence And Machine Learning to increase your presentation threshold. Encompassed with six stages, this template is a great option to educate and entice your audience. Dispence information on Increased Revenue, Resource Cost Savings, Increased Customer Satisfaction using this template. Grab it now to reap its full benefits.
This slide showcase machine learning framework for centric model approach with developing experimental to improve model performance and technological adaption. It include AI designer, AI admins and AI business consumers Introducing our premium set of slides with Persona Centric AI Machine Learning Framework. Elucidate the three stages and present information using this PPT slide. This is a completely adaptable PowerPoint template design that can be used to interpret topics like Deployment, Training, Inferencing, Operationalize, Interpretability. So download instantly and tailor it with your information.
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Machine Learning
Download Machine Learning PowerPoint Templates for your Data Science Presentations. In this category you will find Professional Business Presentation Templates designed for Machine Learning, including PowerPoint themes, PowerPoint Icons and ML metaphors.
Machine Learning is a term used for describing a field in computer science which makes use of statistical methods to create algorithms capable of learning through data, without programming explicit routines or rules. SlideModel.com Machine Learning presentations are created to describe this field of study in a generic overall approach and with a business audience in mind.
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