#Supervised Learning

The project provides Python-based implementations of key machine learning models, focusing on transparent explanations over optimization. Explore examples like Polynomial Regression and CNN Classification. It includes supervised, unsupervised, reinforcement, and deep learning approaches, offering a thorough guide for foundational machine learning exploration.

An objective overview of instruction tuning methodologies, dataset structures, and model training in large language models. This survey evaluates applications across various domains and considers factors impacting outcomes, offering valuable insights into the challenges and future research directions.

Explore a structured 100-day journey into machine learning, covering both supervised and unsupervised techniques. This project involves practical coding exercises in algorithms such as linear regression, k-NN, and support vector machines (SVMs). With resources from Coursera and YouTube, enhance your understanding of key concepts like decision trees and neural networks, fostering a comprehensive grasp of machine learning.

Discover a platform dedicated to the training of AI agents for StarCraft II, featuring both supervised and reinforcement learning options. It supports resource-limited training environments and offers interactive demos. This framework caters to both grandmaster level AI progression and accessible development processes, with detailed setup and test guidance. Suitable for developers aiming to enhance game AI with solid models and structured frameworks.

Discover key machine learning concepts with detailed guides created for Stanford's CS 229. This resource offers vital refreshers on prerequisites and extensive coverage of supervised, unsupervised, and deep learning, including practical tips for model training. Enhance your knowledge with comprehensive compilations and multi-language downloadable PDFs. Ideal for students and professionals aiming for a deep understanding of machine learning. Access the materials on any device via the dedicated site and assist with translations for broader accessibility.

The repository contains detailed solutions and practical notes for the Machine Learning Specialization by Andrew Ng on Coursera. It covers essential topics such as Supervised Learning, Regression, and Advanced Algorithms including Neural Networks. The content is suitable for both beginners and advanced learners, featuring optional labs and quizzes to enhance understanding. It also offers resources on Mathematics for Machine Learning and Data Science, providing a solid theoretical foundation. These materials serve as valuable resources on the journey to mastering machine learning.

This updated 2023-2024 version provides comprehensive notes and practical experiments on deep learning, covering topics such as supervised learning, CNNs, RNNs, transformers, and large language models. It includes downloadable resources, Jupyter notebooks, and guides on key programming tools like Python, PyTorch, and Docker, offering a complete educational experience for mastering modern machine learning methods.