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https://github.com/abhisaphire/machinelearning-courseracourse-stanforduniversity

This repo is for anyone who wants help in understanding and solving Andrew Ng's Coursera Course on Machine Learning Assignments and Quizes. (YEAR 2020)
https://github.com/abhisaphire/machinelearning-courseracourse-stanforduniversity

abhisaphire andrew-ng-course andrew-ng-machine-learning artificial-neural-networks assignment-solutions logistic-regression machine-learning machine-learning-algorithms machine-learning-coursera machine-learning-stanford quiz-solutions quizes svm

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This repo is for anyone who wants help in understanding and solving Andrew Ng's Coursera Course on Machine Learning Assignments and Quizes. (YEAR 2020)

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README 

        
# MachineLearning-CourseraCourse-StanfordUniversity



This repo is for anyone who wants help in understanding and solving Andrew Ng's Coursera Course on Machine Learning Assignments and Quizes. All the answers provided in this repo are provided by me and don't have to be 100% correct.



Language used in Assignments is MATLAB



## Course Objective

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.



This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: 



(i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). 



(ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). 



(iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). 



The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.



## Quick Access

### Week 1 (Introduction and Linear Algebra)

* [Introduction Quiz](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/blob/master/Week-1/Introduction-Quiz.md)

* [Linear Algebra Quiz](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/blob/master/Week-1/LinearAlgebra-Quiz.md)

* [Linear Regression Univariant Quiz](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/blob/master/Week-1/LinearRegression-1var-Quiz.md)

### Week 2 (Linear Regression and Octave/MATLAB)

* [Linear Rgeression Multivariant Quiz](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/blob/master/Week-2/LinearRegressionMultipleVar-Quiz.md)

* [Octave / MATLAB Quiz](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/blob/master/Week-2/OctaveMatlab-Quiz.md)

* [Assignment / Exercise 1](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/tree/master/Week-2/Exercise-1)

### Week 3 (Logistic Regression and Regularization)

* [Logistic Regression Quiz](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/blob/master/Week-3/LogisticRegression-Quiz.md)

* [Assignment / Exercise 2](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/tree/master/Week-3/Exercise-2)

### Week 4 (Neural Networks : Representation)

* [Assignment / Exercise 3](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/tree/master/Week-4)

### Week 5 (Neural Networks : Learning)

* [Assignment / Exercise 4](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/tree/master/Week-5)

### Week 6 (Applying ML and ML System Design)

* [ML System Design Quiz](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/blob/master/Week-6/ML_system_design_QUIZ.md)

* [Advice for applying ML Quiz](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/blob/master/Week-6/advice_for_applying_ML_QUIZ.md)

* [Assignment / Exercise 5](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/tree/master/Week-6/Exercise-5)

### Week 7 (Support Vector Machines)

* [Support Vector Machine Quiz](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/blob/master/Week-7/support_vector_machine.md)

* [Assignment / Exercise 6](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/tree/master/Week-7/Exercise-6)

### Week 8 (Unsupervised Learning and Dimentionality Reduction)

* [Unsupervised Learning Quiz](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/blob/master/Week-8/unsupervised-learning.md)

* [Assignment / Exercise 7](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/tree/master/Week-8/Exercise-7)

### Week 9 (Anomaly Detection and Recommender Systems)

* [Assignment / Exercise 8](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/tree/master/Week-9/Exercise-8)

### Week 10 (Large Scale Machine Learning)

### Week 11 (Photo OCR Application)



## Certification

* [Certificate](https://github.com/AbhiSaphire/MachineLearning-CourseraCourse-StanfordUniversity/blob/master/Certification%20Coursera%20M7T9Q7N64XM6.pdf)