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In this repository I implemented all assignments in python for the purpose of learning python

License: MIT License

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machine-learning deep-learning artificial-neural-networks linear-regression logistic-regression regularization multiclass-classification bias-variance polynomial-regression coursera-machine-learning

coursera-machine-learning's Introduction

[NO LONGER DEVELOPED] Coursera Machine Learning by Andrew Ng

In this repository I implemented few assignments in python. (No octave to python library)

Acknowledgement

I started learning python by learning machine learning at the same time and I may have a lot of inefficient/ugly/... codes here. There are lots of good repos out there for this coarse, please use them.

Features

  1. All optional exercises have been done
  2. All computations done in vectorized form

Schedule

  • Week 2 - Linear Regression
    • Linear Regression With One Variable
    • Linear Regression With Multiple Variable
  • Week 3 - Logistic Regression
    • Logistic Regression
    • Regularized Logistic Regression
  • Week 4 - Multi-Class Classification and Neural Networks
    • Multi-class Classification
    • Neural Networks
    • Trying Different Parameters
  • Week 5 - Neural Networks Learning
    • Neural Networks
    • Backpropagation #3
    • Trying Different Parameters
  • Week 6 - Regularized Linear Regression and Bias v.s. Variance
    • Regularized Linear Regression
    • Bias-Variance
    • Polynomial Regression

Reference

Coursera Machine Learning course by Andrew Ng.

coursera-machine-learning's People

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coursera-machine-learning's Issues

Unregularized or Regularized version of cost function

In the ex3.pdf, instructor says we should write unregularized cost function in the file lrCostFunction but in this file,all comments are about regularized version of cost function. So we implement and compare our result to understand which is correct.
(regularized should be correct because of too many parameters - prevent overfitting)

Optimizer do not work!

Week 5 - Backpropagation
scipy.fmin_cg is not working because of precision error. I think the problem is we should use one dimensional x instead of passing x as a matrix.

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