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Machine-Learning-Stanford-Andrew-Ng

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About Machine-Learning-Stanford-Andrew-Ng

Machine Learning Stanford Andrew Ng is a comprehensive repository containing complete solutions to programming assignments and quizzes for the Machine Learning course on Coursera taught by Andrew Ng of Stanford University. Designed for students and developers, this collection serves as a reference tool to verify understanding of machine learning algorithms. Users are encouraged to attempt all problems independently before consulting the provided code for guidance or debugging support. The project covers the full eleven-week curriculum, including linear and logistic regression, regularization, neural networks for representation and learning, multi-class classification, support vector machines, unsupervised learning, dimensionality reduction via principal component analysis, anomaly detection, recommender systems, and large-scale learning techniques. It also includes lecture slides, quiz solutions, and implementation details for practical applications like photo OCR and machine learning system design. The code

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Machine Learning (Coursera)

This is my solution to all the programming assignments and quizzes of Machine-Learning (Coursera) taught by Andrew Ng. After completing this course you will get a broad idea of Machine learning algorithms. Try to solve all the assignments by yourself first, but if you get stuck somewhere then feel free to browse the code.

Contents

  • Lectures Slides
  • Solution to programming assignment
  • Solution to Quizzes

by Andrew Ng, Stanford University, Coursera

Week 1

  • [X] Videos: Introduction
  • [X] Quiz: Introduction
  • [X] Videos: Linear Regression with One Variable
  • [X] Quiz: Linear Regression with One Variable

Week 2

  • [X] Videos: Linear Regression with Multiple Variables
  • [X] Quiz: Linear Regression with Multiple Variables
  • [X] Videos: Octave/Matlab Tutorial
  • [X] Quiz: Octave/Matlab Tutorial
  • [X] Programming Assignment: Linear Regression

Week 3

  • [X] Videos: Logistic Regression
  • [X] Quiz: Logistic Regression
  • [X] Videos: Regularization
  • [X] Quiz: Regularization
  • [X] Programming Assignment: Logistic Regression

Week 4

  • [X] Videos: Neural Networks: Representation
  • [X] Quiz: Neural Networks: Representation
  • [X] Programming Assignment: Multi-class Classification and Neural Networks

Week 5

  • [X] Videos: Neural Networks: Learning
  • [X] Quiz: Neural Networks: Learning
  • [X] Programming Assignment: Neural Network Learning

Week 6

  • [X] Videos: Advice for Applying Machine Learning
  • [X] Quiz: Advice for Applying Machine Learning
  • [X] Videos: Programming Assignment: Regularized Linear Regression and Bias/Variance
  • [X] Machine Learning System Design
  • [X] Quiz: Machine Learning System Design

Week 7

  • [X] Videos: Support Vector Machines
  • [X] Quiz: Support Vector Machines
  • [X] Programming Assignment: Support Vector Machines

Week 8

  • [X] Videos: Unsupervised Learning
  • [X] Quiz: Unsupervised Learning
  • [X] Videos: Dimensionality Reduction
  • [X] Quiz: Principal Component Analysis
  • [X] Programming Assignment: K-Means Clustering and PCA

Week 9

  • [X] Videos: Anomaly Detection
  • [X] Quiz: Anomaly Detection
  • [X] Videos: Recommender Systems
  • [X] Quiz: Recommender Systems
  • [X] Programming Assignment: Anomaly Detection and Recommender Systems

Week 10

  • [X] Videos: Large Scale Machine Learning
  • [X] Quiz: Large Scale Machine Learning

Week 11

  • [X] Videos: Application Example: Photo OCR
  • [X] Quiz: Application: Photo OCR

Certificate

  • [Verified Certificate]()

References

[1] Machine Learning - Stanford University