ml-tutorials

Machine learning tutorials: easy-to-follow tutorials, offering a practical approach to understanding the intricacies of various machine learning techniques.

A Jupyter Notebook dedicated to each algorithm, providing concise hands-on code snippets to facilitate hands-on learning.

data_augmentation.ipynb

There are two general methods for balancing data: majority undersampling, and minority oversampling. This notebook covers random undersampling and oversampling and expands on the latter with SMOTE (Synthetic Minority Over-sampling Technique).

RL_examples: reinforcement learning

tf-idf and word2vec comparison

Simple SVD-based (Singular Value Decomposition) recommender system on a toy dataset