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https://github.com/krasserm/machine-learning-notebooks

Stanford Machine Learning course exercises implemented with scikit-learn
https://github.com/krasserm/machine-learning-notebooks

machine-learning scikit-learn

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Stanford Machine Learning course exercises implemented with scikit-learn

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README 

        
## Machine learning notebooks



This project contains solutions to the [Stanford Machine Learning](https://www.coursera.org/learn/machine-learning) 

course exercises implemented with [Python](https://www.python.org/) and [scikit-learn](http://scikit-learn.org/). The scikit-learn 

machine learning library provides optimized implementations for all algorithms presented in the course and needed in 

the course exercises. Instead of writing low-level [Octave](https://www.gnu.org/software/octave/) code, as required by 

the course, the solutions presented here demonstrate how to use scikit-learn to solve these exercises on a much higher 

level. It is a level that is closer to that of real-world machine learning projects. This project respects the 

[Coursera Honor Code](https://learner.coursera.help/hc/en-us/articles/209818863-Coursera-Honor-Code) as the presented 

solutions can't be used to derive the lower-level Octave code that must be written to complete the assignments. 



I developed these solutions while learning Python and its 

[scientific programming libraries](https://www.scipy.org/) such as [NumPy](http://www.numpy.org/), 

[SciPy](https://scipy.org/scipylib/index.html), [pandas](http://pandas.pydata.org/) and 

[matplotlib](http://matplotlib.org/) in a machine learning context. The solutions are provided as 

[Jupyter](http://jupyter.org/) notebooks. Developers new to scikit-learn hopefully find them useful to see how 

the machine learning topics covered in the course relate to the 

[scikit-learn API](http://scikit-learn.org/stable/modules/classes.html). In their current state, the notebooks neither 

explain machine learning basics nor introduce the used libraries. For learning machine learning basics I highly 

recommend attending the course lectures. For an introduction to the used libraries the following tutorials are a good 

starting point: 



- [Python tutorial](https://docs.python.org/3/tutorial/)

- [NumPy tutorial](https://numpy.org/doc/stable/user/quickstart.html)

- [SciPy tutorial](https://docs.scipy.org/doc/scipy/reference/tutorial/index.html)

- [Pandas tutorial](http://pandas.pydata.org/pandas-docs/stable/10min.html)

- [Pyplot tutorial](http://matplotlib.org/users/pyplot_tutorial.html)

- [Scikit-learn tutorials](http://scikit-learn.org/stable/tutorial/index.html)



### Course exercises



- [Exercise 1 notebook](ml-ex1.ipynb): Linear regression ([ex1.pdf](data/ml-ex1/ex1.pdf))

- [Exercise 2 notebook](ml-ex2.ipynb): Logistic regression ([ex2.pdf](data/ml-ex2/ex2.pdf))

- [Exercise 3 notebook](ml-ex3.ipynb): Multi-class classification and neural networks ([ex3.pdf](data/ml-ex3/ex3.pdf))

- [Exercise 4 notebook](ml-ex4.ipynb): Neural networks learning ([ex4.pdf](data/ml-ex4/ex4.pdf))

- [Exercise 5 notebook](ml-ex5.ipynb): Regularized linear regression and bias vs. variance ([ex5.pdf](data/ml-ex5/ex5.pdf))

- [Exercise 6 notebook](ml-ex6.ipynb): Support vector machines ([ex6.pdf](data/ml-ex6/ex6.pdf))

- [Exercise 7 notebook](ml-ex7.ipynb): K-means clustering and principal component analysis ([ex7.pdf](data/ml-ex7/ex7.pdf))

- [Exercise 8 notebook](ml-ex8.ipynb): Anomaly detection and recommender systems ([ex8.pdf](data/ml-ex8/ex8.pdf))