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Coursera: Machine Learning - All weeks solutions [Assignment + Quiz] - Andrew NG
![Coursera: Machine Learning - All weeks solutions [Assignment + Quiz] - Andrew NG](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEih0B0NpVY1WWh5W-aPiZ5fckewDLdTtMqzpdakJYFDfq421qiuG_LyCqsaUXx-1Eqjb65iUZUtLzYTInm6843femcLYi3owzOlYr2qvS7XRvlrkLbRLK-vPoqV5xw-Tl68jidbeUT650I/w320-h200/Coursera+Machine+Learning+-+All+weeks+solutions+%255BAssignment+%252B+Quiz%255D+-+Andrew+NG.webp "Coursera: Machine Learning - All weeks solutions [Assignment + Quiz] - Andrew NG")
Recommended Machine Learning Courses: Coursera: Machine Learning Coursera: Deep Learning Specialization Coursera: Machine Learning with Python Coursera: Advanced Machine Learning Specialization Udemy: Machine Learning LinkedIn: Machine Learning Eduonix: Machine Learning edX: Machine Learning Fast.ai: Introduction to Machine Learning for Coders
=== Week 1 ===
Assignments: .
- No Assignment for Week 1
- Machine Learning (Week 1) Quiz ▸ Introduction
- Machine Learning (Week 1) Quiz ▸ Linear Regression with One Variable
- Machine Learning (Week 1) Quiz ▸ Linear Algebra
=== Week 2 ===
Assignments:.
- Machine Learning (Week 2) [Assignment Solution] ▸ Linear regression and get to see it work on data.
- Machine Learning (Week 2) Quiz ▸ Linear Regression with Multiple Variables
- Machine Learning (Week 2) Quiz ▸ Octave / Matlab Tutorial
=== Week 3 ===
- Machine Learning (Week 3) [Assignment Solution] ▸ Logistic regression and apply it to two different datasets
- Machine Learning (Week 3) Quiz ▸ Logistic Regression
- Machine Learning (Week 3) Quiz ▸ Regularization
=== Week 4 ===
- Machine Learning (Week 4) [Assignment Solution] ▸ One-vs-all logistic regression and neural networks to recognize hand-written digits.
- Machine Learning (Week 4) Quiz ▸ Neural Networks: Representation
=== Week 5 ===
- Machine Learning (Week 5) [Assignment Solution] ▸ Back-propagation algorithm for neural networks to the task of hand-written digit recognition.
- Machine Learning (Week 5) Quiz ▸ Neural Networks: Learning
=== Week 6 ===
- Machine Learning (Week 6) [Assignment Solution] ▸ Regularized linear regression to study models with different bias-variance properties.
- Machine Learning (Week 6) Quiz ▸ Advice for Applying Machine Learning
- Machine Learning (Week 6) Quiz ▸ Machine Learning System Design
=== Week 7 ===
- Machine Learning (Week 7) [Assignment Solution] ▸ Support vector machines (SVMs) to build a spam classifier.
- Machine Learning (Week 7) Quiz ▸ Support Vector Machines
=== Week 8 ===
- Machine Learning (Week 8) [Assignment Solution] ▸ K-means clustering algorithm to compress an image. ▸ Principal component analysis to find a low-dimensional representation of face images.
- Machine Learning (Week 8) Quiz ▸ Unsupervised Learning
- Machine Learning (Week 8) Quiz ▸ Principal Component Analysis
=== Week 9 ===
- Machine Learning (Week 9) [Assignment Solution] ▸ Anomaly detection algorithm to detect failing servers on a network. ▸ Collaborative filtering to build a recommender system for movies.
- Machine Learning (Week 9) Quiz ▸ Anomaly Detection
- Machine Learning (Week 9) Quiz ▸ Recommender Systems
=== Week 10 ===
- No Assignment for Week 10
- Machine Learning (Week 10) Quiz ▸ Large Scale Machine Learning
=== Week 11 ===
- No Assignment for Week 11
- Machine Learning (Week 11) Quiz ▸ Application: Photo OCR Variables
Question 5 Your friend in the U.S. gives you a simple regression fit for predicting house prices from square feet. The estimated intercept is -44850 and the estimated slope is 280.76. You believe that your housing market behaves very similarly, but houses are measured in square meters. To make predictions for inputs in square meters, what intercept must you use? Hint: there are 0.092903 square meters in 1 square foot. You do not need to round your answer. (Note: the next quiz question will ask for the slope of the new model.) i dint get answer for this could any one plz help me with it
Please comment below specific week's quiz blog post. So that I can keep on updating that blog post with updated questions and answers.
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Good day Akshay, I trust that you are doing well. I am struggling to pass week 2 assignment, can you please assist me. I am desperate to pass this module and I am only getting 0%... Thank you, I would really appreat your help.
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Data Science (Archived) — Mojib Chawdhury asked a question.
What’s the correct answer for quiz question 3,4 for week 2. I think there are some problem in these two questions’ answers. None of the selection option of MCQ is showing as correct answer. It made me confused. Please let me know which are the correct answer and why. If there are any technological problem in the answer( for which it never shows the answer to be correct ) please mend it. Thanks
- Data Science
SuprakashSen
Request to grade my assignment
https://www.coursera.org/learn/machine-learning-with-python/peer/bsfc4/the-best-classifier/review/mLVrupEyEeqbkhLvDNasHQ
Mojib Chawdhury
Thanks in advance for the future respondent. I already figure out the problem and solved it. Thanks.
I have the same problem what should I do ?
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The following algorithms are used to build models for the different datasets: k-Nearest Neighbour, Decision Tree, Support Vector Machine, Logistic Regression The results is reported as the accuracy of each classifier, using the following metrics when these are applicable: Jaccard index, F1-score, Log Loss. This project counts towards the final grade of the course. - skhiearth/Coursera-IBM ...
An unfortunate aspect of this class is that the programming assignments are in MATLAB or OCTAVE, probably because this class was made before python became the go-to language in machine learning. The Python machine learning ecosystem has grown exponentially in the past few years, and is still gaining momentum.
With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms.
Write an unsupervised learning algorithm to Land the Lunar Lander Using Deep Q-Learning. The Rover was trained to land correctly on the surface, correctly between the flags as indicators after many unsuccessful attempts in learning how to do it. The final landing after training the agent using appropriate parameters : lunar_lander.mp4
In this module, you will learn about applications of Machine Learning in different fields such as health care, banking, telecommunication, and so on. You'll get a general overview of Machine Learning topics such as supervised vs unsupervised learning, and the usage of each algorithm. Also, you understand the advantage of using Python ...
If the issue persists, it's likely a problem on our side. Unexpected end of JSON input. keyboard_arrow_up. content_copy. SyntaxError: Unexpected end of JSON input. Refresh. Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources.
There are 4 modules in this course. This course will give you an introduction to machine learning with the Python programming language. You will learn about supervised learning, unsupervised learning, deep learning, image processing, and generative adversarial networks. You will implement machine learning models using Python and will learn ...
dfiam / Machine Learning with Python Week 6 Final.ipynb. dfiam. /. Machine Learning with Python Week 6 Final.ipynb. Created 4 years ago. Star 0. Fork 2. GitHub Gist: instantly share code, notes, and snippets.
This repo presents some solutions to the labs of this course, that dives into the basics of machine learning using an approachable, and well-known programming language, Python.. The works were done for the following laboratories: Simple Linear Regression; Multiple Linear Regression
Learn How to Sign up to Coursera courses for free. 1700 Coursera Courses That Are Still Completely Free. Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning. This course will begin with a gentle ...
Coursera - Machine Learning with Python By IBM - All Quiz & Peer Graded Assignment Answers | Complete Certification In One Video For FREE Subscribe Channel ...
Unlock the secrets of Machine Learning with Python in IBM's comprehensive Coursera course! 🚀 In this video, we've compiled all the quiz and assignment answe...
Specialization - 3 course series. In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Starting with supervised learning, we will cover ...
Click here to see solutions for all Machine Learning Coursera Assignments. Click here to see more codes for Raspberry Pi 3 and similar Family. Click here to see more codes for NodeMCU ESP8266 and similar Family. Click here to see more codes for Arduino Mega (ATMega 2560) and similar Family. Feel free to ask doubts in the comment section. I will try my best to answer it.
Machine Learning with Python final assignment Building model using KNN, finding the best k and accuracy evaluation Building model using Decision Tree and find the accuracy evaluation
If the issue persists, it's likely a problem on our side. Unexpected token < in JSON at position 4. keyboard_arrow_up. content_copy. SyntaxError: Unexpected token < in JSON at position 4. Refresh. Explore and run machine learning code with Kaggle Notebooks | Using data from [Private Datasource]
Machine learning with python week 2 quiz Q 3,4. What's the correct answer for quiz question 3,4 for week 2. I think there are some problem in these two questions' answers. None of the selection option of MCQ is showing as correct answer. It made me confused.
This course is part of the Applied Data Science with Python Specialization. When you enroll in this course, you'll also be enrolled in this Specialization. Learn new concepts from industry experts. Gain a foundational understanding of a subject or tool. Develop job-relevant skills with hands-on projects.
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Find helpful learner reviews, feedback, and ratings for Machine Learning with Python from IBM. Read stories and highlights from Coursera learners who completed Machine Learning with Python and wanted to share their experience. I'm extremely excited with what I have learnt so far. As a newbie in Machine Learning, the exposure ...
A repository with solutions to the assignments on Andrew Ng's machine learning MOOC on Coursera - suraggupta/coursera-machine-learning-solutions-python
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story. Create different types of charts and plots such as line, area, histograms, bar, pie, box, scatter, and bubble. Create advanced visualizations such as waffle charts, word clouds, regression plots, maps ...
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