Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
This hands-on guide teaches building intelligent systems with scikit-learn, Keras, and TensorFlow. Track ML projects end-to-end, explore supervised models like SVMs and random forests, unsupervised techniques including clustering and anomaly detection, and neural architectures such as CNNs, RNNs, GANs, and transformers. Apply them to computer vision, NLP, generative models, and reinforcement learning.
About This Book
This book provides a comprehensive guide to building intelligent systems using key machine learning libraries. It covers tracking an example ML project from end to end with scikit-learn, offering practical insights into implementation.
Readers will explore a variety of supervised models, including support vector machines, decision trees, random forests, and ensemble methods, to understand their applications and strengths.
The text delves into unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection, enabling effective data analysis without labeled inputs.
A significant focus is on neural network architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers, with detailed explanations of their structures and uses.
Using TensorFlow and Keras, the book instructs on building and training neural networks for diverse tasks like computer vision, natural language processing, generative models, and deep reinforcement learning.
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