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Implementations in python of methods and programming assignments of course Machine Learning of Coursera by Andrew Ng
| Date | Stars |
|---|---|
| 2026-07-31 | 406 |
| 2026-08-06 | 407 |
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# Machine Learning with Andrew Ng Programming assignments that I implemented in python of [Coursera's Machine Learning Course](https://www.coursera.org/learn/machine-learning) (it uses Octave/MATLAB). I also added some concepts and formulas that I think are useful to help to understand the algorithms. In order to have a nice visualization of the concepts, formulas, codes and exercises, I did all the implementations in [Jupyter Notebooks](https://jupyter.org/). ### Programming Assignments Notebooks: [Programming Exercise 1 - Linear Regression](https://nbviewer.jupyter.org/github/susilvaalmeida/machine-learning-andrew-ng/blob/master/Programming%20Exercise%201%20-%20Linear%20Regression.ipynb) <br> [Programming Exercise 2 - Logistic Regression](https://nbviewer.jupyter.org/github/susilvaalmeida/machine-learning-andrew-ng/blob/master/Programming%20Exercise%202%20-%20Logistic%20Regression.ipynb) <br> [Programming Exercise 3 - Multi-class Classification and Neural Networks](https://nbviewer.jupyter.org/github/susilvaalmeida/machine-learning-andrew-ng/blob/master/Programming%20Exercise%203%20-%20Multi-class%20Classification%20and%20Neural%20Networks.ipynb) <br> [Programming Exercise 4 - Neural Networks Learning](https://nbviewer.jupyter.org/github/susilvaalmeida/machine-learning-andrew-ng/blob/master/Programming%20Exercise%204%20-%20Neural%20Network%20Learning.ipynb) <br> [Programming Exercise 5 - Regularized Linear Regression and Bias vs Variance](https://nbviewer.jupyter.org/github/susilvaalmeida/machine-learning-andrew-ng/blob/master/Programming%20Exercise%205%20-%20Regularized%20Linear%20Regression%20and%20Bias%20vs%20Variance.ipynb) <br> [Programming Exercise 6 - Support Vector Machines](https://nbviewer.jupyter.org/github/susilvaalmeida/machine-learning-andrew-ng/blob/master/Programming%20Exercise%206%20-%20Support%20Vector%20Machines.ipynb) <br> [Programming Exercise 7 - K-Means Clustering and Principal Component Analysis](https://nbviewer.jupyter.org/github/susilvaalmeida/machine-learning-andrew-ng/blob/master/Programming%20Exercise%207%20-%20K-means%20Clustering%20and%20Principal%20Component%20Analysis.ipynb) <br> [Programming Exercise 8 - Anomaly Detection and Recommender Systems](https://nbviewer.jupyter.org/github/susilvaalmeida/machine-learning-andrew-ng/blob/master/Programming%20Exercise%208%20-%20Anomaly%20Detection%20and%20Recommender%20Systems.ipynb)
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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