#Scikit-Learn

This comprehensive repository includes 35 Python notebooks focusing on data manipulation, classical machine learning, computer vision, and NLP. It offers practical guidance on MLOps, TensorFlow, and Scikit-Learn, enhanced by hands-on exercises. Topics encompass deep learning architectures, data analysis, visualization, and model deployment. Regular updates include transfer learning methods and advanced neural network techniques, beneficial for data scientists and machine learning engineers.

Delve into a practical and detailed guide on machine learning, focusing on PyTorch and Scikit-Learn's methods and applications. This book shares expert insights on algorithm training, data handling, and model assessment, while venturing into complex areas such as neural networks, deep learning, and reinforcement learning. With code examples and step-by-step instructions, it enhances practical ML knowledge, supported by a comprehensive code notebook repository. Authored by experts Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili, it is a vital resource for anyone looking to expand their understanding of machine learning with PyTorch and Scikit-Learn.

Discover the basics of Machine Learning with Python using practical examples and interactive notebooks. This project accompanies the first edition of 'Hands-On Machine Learning with Scikit-Learn and TensorFlow,' including exercises and code solutions. The notebooks can be accessed online through Colaboratory, Binder, or Deepnote, and executed locally with an Anaconda setup recommended for Python 3.7. It offers a thorough educational experience for those interested in applying machine learning principles through practical scenarios.

Published by Packt, this repository houses comprehensive Python examples from the 'Python Machine Learning, 2nd Edition' book. It encompasses crucial machine learning topics including classification, data processing, ensemble learning, and deep learning with TensorFlow. Tailored for both beginners and experienced practitioners, each chapter delivers practical guidance enriched with theoretical understanding. This edition enhances graphical elements, resolves previous inaccuracies, and introduces new sections on managing imbalanced data and deep learning methods.

Discover the extensive code repository of 'Python Machine Learning, 3rd Ed.', a crucial resource for understanding machine learning with Python and TensorFlow. This edition encompasses topics like classification, regression, clustering, neural networks, and reinforcement learning. It offers practical advice on data pre-processing, model evaluation, and hyperparameter optimization, featuring hands-on examples for real-world applications like sentiment analysis and image classification.