Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch

This book provides a comprehensive guide to machine learning and deep learning with Python, covering tools such as NumPy, Pandas, Matplotlib, Scikit-learn, and PyTorch. It is divided into three sections, covering number crunching, machine learning basics, and complex neural network architectures, and includes a detailed end-to-end solution with neural networks in PyTorch.

Format: Paperback / softback
Length: 335 pages
Publication date: 06 March 2022
Publisher: APress

This comprehensive guide is designed for readers with basic to intermediate knowledge of machine learning and deep learning, introducing essential tools and libraries in Python. It serves as a valuable long-term reference manual for practitioners seeking solutions to common scenarios. The book is divided into three sections:

Section 1: Number Crunching and Data Analysis Tools
In this section, readers will learn about number crunching and data analysis tools using Python. It covers environment configuration, data loading, numerical processing, data analysis, and visualizations. The in-depth explanations will help readers understand the fundamentals of data manipulation and analysis.

Section 2: Machine Learning Basics and Scikit-Learn Library
This section delves into machine learning basics and the Scikit-learn library. It explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, as well as ensemble learning methods. The theoretical and practical lessons make it easy for readers to grasp these concepts and apply them in real-world scenarios.

Section 3: Complex Neural Network Architectures
In this section, readers will explore complex neural network architectures with details on their internal workings and implementation. It covers convolutional neural networks and Recurrent neural networks, providing a deep understanding of these powerful tools for solving complex problems.

The final chapter offers a detailed end-to-end solution with neural networks in Pytorch, allowing readers to apply their knowledge to build and train neural networks for various applications.

By completing Hands-on Machine Learning with Python, readers will gain the skills to implement machine learning and neural network solutions and extend them to their advantage. Whether you are a data scientist, engineer, or researcher, this guide will provide you with the foundation you need to succeed in the field of machine learning and deep learning.

Weight: 672g
Dimension: 177 x 253 x 24 (mm)
ISBN-13: 9781484279205
Edition number: 1st ed.