Machine Learning Tutorials

This repo consists of different Jupyter Notebooks demonstrating how to use different machine learning techniques. New notebooks will continue to be pushed, as this repo is still active. For any requests or feedback, please use GitHub Issues.

Getting Started

Please go through the setup steps, if you do not have Python or the dependencies installed, prior to running the code yourself.

If you are new to machine learning, I recommend going through the Jupyter Notebooks in this order:

1. Introduction to Machine Learning
2. Linear Regression
3. Logistic Regression
4. Evaluation Metrics
5. Clustering
6. ARIMA
7. Anomaly Detection
8. Generalized Linear Models
9. Gradient Descent
10. N-Grams
11. Neural Attention