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Plain python implementations of basic machine learning algorithms

License: MIT License

Jupyter Notebook 100.00%

machine-learning logistic-regression ipynb machine-learning-algorithms linear-regression perceptron python-implementations kmeans algorithm python3

machine_learning_basics's Introduction

Machine learning basics

This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations.

Data preprocessing

After several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, not to provide the most efficient implementations.

Live demo

Run the notebooks online without having to clone the repository or install jupyter: .

Note: this does not work for the data_preprocessing.ipynb and image_preprocessing.ipynb notebooks because they require downloading a dataset first.

Feedback

If you have a favorite algorithm that should be included or spot a mistake in one of the notebooks, please let me know by creating a new issue.

License

See the LICENSE file for license rights and limitations (MIT).

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machine_learning_basics's Issues

well done, hymn!

very good.

in chapter "linear-regression":

"n_iters" is omitted in section "training with gradient descent";

"c='orange'" is omitted in section "visualize test predictions";

in chapter "logistic-regression":

"* 100" is omitted in section "testing the model";

in chapter "k-nearest-neighbor":

"regregression" may be typo at the start;

"plt.show()" is omitted in section "dataset";

"test accuracy with k = 8" and "*8-like" characters may be typos, must be 4;

in chapter "simple neural net":

"\textit{sigmoid}" may be typo in section "forward pass";

in chapter "softmax regression":

"one-hot encoded" in section "step iii", confused by "So $y_k^{(i)}$ is $1$ is the target class for $\boldsymbol{x}^{(i)}$ is k, otherwise $y_k^{(i)}$ is $0$.", what is the meaning of "is 1 is the target...";

continuous updating...

Data Preprocessing

I think you should also mention about data preprocessing. It is one of the important and basic step of machine learning.

Naive bayes

Would be nice to see a naive bayes implementation.

not a issue but a request!

Thanks for these clear tutorials for basics of machine learning.

I have read through with great honor and translated into Chinese, and errata #7 as well.

Now i wish to contribute them as a Chinese version, just put this link into README.md can come true.