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Coursera Programming Exercises from Week 2 to Week 9. Please do not copy the code directly from the website. Try it on your own and solve it.

License: MIT License

linear-regression gradient-descent feature normalization data-plotting logistic-regression neural-networks support-vector-machines support-vector-regression clustering

ml-stanford-university's Introduction

View Coursera-Machine-Learning-Andrew-NG on File Exchange

ML-Stanford-University

This is my solution to all the programming assignments of Machine-Learning (Coursera) taught by Andrew Ng. After completing this course you will get a broad idea of Machine learning algorithms.This is a repository of my coursera Machine Learning by Standford, Andrew NG course's assignments' solution This is all written in matlab. The datasets are also uploaded with the excercises

Includes:

  • Linear Regression (one variable and multiple variables)
  • Logistic Regression
  • Regularization
  • Neural networks
  • Support Vector Machine
  • Clustering
  • Dimensionality Reduction
  • Anomaly Detection
  • Recommender System

Contents

  • Solution to the programming quiz
  • Instruction PDF

Octave

Octave is a high level (open source) programming language similar to Matlab.

Stanford Honor Code

"We strongly encourage students to form study groups, and discuss the lecture videos (including in-video questions). We also encourage you to get together with friends to watch the videos together as a group. However, the answers that you submit for the review questions should be your own work. For the programming exercises, you are welcome to discuss them with other students, discuss specific algorithms, properties of algorithms, etc.; we ask only that you not look at any source code written by a different student, nor show your solution code to other students."

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Reference:

Machine Learning - Stanford University(Coursera)

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