Machine Learning Basics

This project goes through fundamental machine learning concepts and algorithms. The materials are from www.suanlab.com.

Getting Started To get started with the project: Install Python and the Scikit-Learn, NumPy and SciPy libraries. Review the Jupyter notebooks in each chapter folder. Try running and modifying the code samples.

Chapters

1. Machine Learning Fundamentals - Covers basic ML concepts like training/test splits, bias/variance, overfitting/underfitting.
2. Scikit-Learn library - Introduction to the Scikit-Learn ML library in Python.
3. Linear Models - Linear regression and ridge/lasso regularization.
4. Logistic Regression - For binary classification.
5. Support Vector Machine - For classification and regression.
6. k Nearest Neighbor - Simple non-parametric method for classification and regression.
7. Naive Bayes - Probabilistic classifier based on Bayes' theorem.
8. Decision Tree - Non-linear model for classification and regression.
9. Ensemble - Methods like random forests and gradient boosting.
10. Clustering - Unsupervised learning with k-Means, hierarchical clustering.
11. Manifold Learning - Dimensionality reduction with PCA and t-SNE.
12. Decomposition - Matrix factorization with SVD and NMF.
13. Recommender System - Collaborative filtering with nearest neighbors and matrix factorization.

Acknowledgments Please visit the original site(www.suanlab.com) for more in-depth tutorials and resources.