This project provides a collection of practical machine learning code examples, including implementations for supervised, unsupervised, and reinforcement learning algorithms. It features deep learning model implementations for convolutional, recurrent, and generative architectures, alongside specific examples of reinforcement learning agents that maximize rewards in simulated environments.
The repository includes dedicated data preprocessing pipelines for sanitization, feature scaling, and dimensionality reduction. It also provides implementations for a wide range of specific models, such as random forests, support vector machines, autoencoders, and generative adversarial networks.
Broad capability areas cover the entire machine learning lifecycle, including data engineering, model evaluation through cross-validation, hyperparameter tuning, and MLOps deployment workflows. It also incorporates mathematical foundations like linear algebra and differential calculus.
The project is delivered as a set of Jupyter Notebooks and includes configurations for containerized environments to ensure consistent execution of the examples.
