Book Review: Machine Learning with PyTorch and Scikit-Learn - insideBIGDATA

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.