Loading repository data…
Sugumaran-Balasubramaniyan / repository
Comprehensive Jupyter notebooks covering ML fundamentals — preprocessing, supervised regression, classification, and model interpretation
This GitHub repository contains a series of Jupyter notebooks covering fundamental concepts of machine learning, from data preprocessing to supervised regression and classification, and model interpretation. Designed for beginners and advanced users, it provides a comprehensive guide to building and evaluating machine learning models.
"Principles of Machine Learning", introduces the basic concepts of machine learning, including the types of problems that machine learning can solve, the different types of machine learning algorithms, and the basics of model selection.
"Data preprocessing", covers the steps involved in preparing data for machine learning. This includes data cleaning, data transformation, feature engineering, and feature scaling.
"Supervised regression", focuses on supervised regression algorithms. This notebook covers linear regression, polynomial regression, and other regression techniques. It also covers model evaluation and validation techniques.
"Supervised classification", covers supervised classification algorithms, including logistic regression, decision trees, and support vector machines. This notebook also covers model evaluation and validation techniques.
"Model interpretation", covers techniques for interpreting and understanding machine learning models. This includes feature importance analysis, partial dependence plots, and model visualization techniques.
Overall, this repository provides a comprehensive introduction to machine learning, covering the most important concepts and techniques for building and evaluating machine learning models. The notebooks are designed to be accessible to beginners, but also provide enough detail to be useful for more advanced users.
Selected from shared topics, language and repository description—not editorial ratings.
ssarka4894 /
A comprehensive Retrieval-Augmented Generation (RAG) system specifically designed for GPU-accelerated data science education. The system combines web documentation, Jupyter notebooks, and Python code examples to provide accurate, well-cited responses about CUDA programming, RAPIDS, PyTorch, TensorFlow, and other GPU computing frameworks.