This repository is a collection of implementation references and solved notebooks covering supervised, unsupervised, and reinforcement learning techniques. It provides practical guides for building predictive models, clustering algorithms, and autonomous agents.
The project includes specific implementations for neural network architectures, such as multi-layer perceptrons for digit recognition, and recommender systems using collaborative and content-based filtering. It also features reinforcement learning systems that utilize deep Q-learning to optimize decision-making policies.
The codebase covers a broad range of machine learning capabilities, including linear and logistic regression, decision tree modeling, and multiclass classification. It also implements unsupervised learning workflows through K-means clustering and Gaussian anomaly detection. Support for model evaluation is provided via bias and variance analysis, decision boundary visualization, and regularization techniques to prevent overfitting.
The project is implemented as a series of Jupyter Notebooks.
