Machine Learning with Scikit-Learn and TensorFlow

Machine Learning with Scikit-Learn and TensorFlow: A Hands-On Guide to Scikit-Learn and TensorFlow for Real World AI
This book is the definitive, hands-on guide for developers and data scientists looking to master the end-to-end Machine Learning pipeline. Starting with the foundational principles of data representation, statistics, and optimization (calculus, gradient descent), the book provides a comprehensive journey across the entire ML landscape. Part I focuses on classical methods using Scikit-Learn, covering linear models, evaluation metrics (ROC, AUC, F1-Score), Support Vector Machines, and powerful ensemble techniques like Random Forests and Gradient Boosting. Part II shifts entirely to Deep Learning with TensorFlow and Keras, tackling the instability of deep networks (vanishing/exploding gradients) using modern solutions like Batch Normalization and Transfer Learning. Readers will learn to architect specialized networks, including Convolutional Neural Networks (CNNs) for vision, Recurrent Neural Networks (RNNs) for sequence processing, and Generative Adversarial Networks (GANs) for creating new data. The final section addresses production readiness, detailing scalable data pipelines (tf.data), distributed training strategies, and deployment using the SavedModel format, TensorFlow Serving, and TensorFlow Lite for edge devices. This guide ensures practitioners can not only build sophisticated models but also deploy and monitor them reliably at scale.