PyTorch and Scikit-Learn for Machine Learning: A Python Guide is the ideal resource for both aspiring and experienced machine learning practitioners who want to learn how to effectively utilize the popular PyTorch and Scikit-Learn libraries in their projects. The book covers the fundamental concepts of machine learning and deep learning, providing clear and concise explanations that are easy to understand. With a focus on hands-on implementation, the book provides practical guidance for building and deploying machine learning models using PyTorch and Scikit-Learn. Readers will learn how to use these libraries to build complex models, preprocess and clean data, and evaluate the performance of their models. The book also covers advanced topics such as deep reinforcement learning and transfer learning, allowing readers to expand their knowledge and skills.

This book takes you on a journey from the origins of machine learning to the latest deep learning architectures. Through conceptual and practical examples, you'll develop a repertoire of techniques that allow you to solve a wide range of predictive modeling tasks, including tabular, image, and text data. PyTorch is a very powerful and versatile tool, and deep learning naturally requires very flexible building blocks. Hence, PyTorch can sometimes be very verbose compared to traditional machine learning libraries such as scikit-learn. In this book, we explain how PyTorch works and cover all the essential parts.

The enticing new title courtesy of Packt Publishing, "Machine Learning with PyTorch and Scikit-Learn," by Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili is a welcome addition to any data scientist's list of learning resources. This 2022 tome consists of 741 well-crafted pages designed to provide a comprehensive framework for working in the realm of machine learning and deep learning. The book is brimming over with topics that will propel you to a leading-edge understanding of the field. Topical areas include an introduction to ML including a simple implementation of perceptron algorithm, data munging, dimensionality reduction, a tour of classification algorithms (logistic regression, SVM, decision tree, KNN), model evaluation and hyperparameter tuning, ensemble learning, regression, sentiment analysis, and unsupervised learning with clustering. The book then shifts into high gear with a number of contemporary topics in deep learning, all using the popular PyTorch framework: implementing a simple multi-layer ANN, parallelizing neural network training, image classification with CNNs, modeling sequential data with RNNs, transformers and NLP, GANs, graph neural networks, and reinforcement learning.

You may wonder, with TensorFlow remaining a prominent framework in the deep learning industry, why we bothered to write a PyTorch book of the Python Machine Learning series, Machine Learning with PyTorch and Scikit-Learn. As a matter of fact, PyTorch has become the most widely-used deep learning framework in the academic and research community. To examine this further, let me provide an up-to-date and more comprehensive comparison between PyTorch and TensorFlow. Supporting dynamic computational graphs is an advantage of PyTorch over TensorFlow. The computational graphs in PyTorch are built on-demand compared to their static TensorFlow counterparts. This makes PyTorch more debug-friendly: you can execute the code line by line while having full access to all variables.

My name is Sebastian, and I am a machine learning and AI researcher with a strong passion for education. As Lead AI Educator at Grid.ai, I am excited about making AI & deep learning more accessible and teaching people how to utilize AI & deep learning at scale. I am also an Assistant Professor of Statistics at the University of Wisconsin-Madison and author of the bestselling book Python Machine Learning.