uranus

The fundamentals of Machine Learning and Deep Learning from Scratch and from other libs such as numpy, panda, matplotlib, scipy, scikit-learn, Keras, Tensorflow, Theano, etc

Table Of Contents

1. Basic Module (Visualizing Data, Linear Algebra, Statistics, Probability, Hypothesis and Inference, Gradient Descent)

2. Decision Tree (ID3, CART) and Random Forest

3. Naive Bayes and Linear Regression

4. Conditional Random Fields (CRF)

5. AdaBoost

6. Hidden Markov Model

7. GMM

8. SGMM

9. Support Vector Machine

10. k-nearest neighbor (KNN)

11. K-mean

12. LogisticRegression

13. Apriori

14. Expectation–Maximization

15. PageRank

16. Deep Neural Network (DNN)

17. Evaluation Metrics: Receiver Operating Characteristic curve (ROC), Precision and recall, Mean Absolute Error (MAE), Root Mean Square Error (RMSE)

References

https://github.com/joelgrus/data-science-from-scratch

https://github.com/justmarkham/scikit-learn-videos

https://github.com/ageron/handson-ml

https://github.com/leriomaggio/deep-learning-keras-tensorflow