Three best books to learn Machine Learning  with downloading links

 

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.

Drive link:  https://drive.google.com/file/d/1LA8V3gEfrBCcIAjoUxEQRFYy21l-mACH/view?usp=sharing

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Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

Drive link:  https://drive.google.com/file/d/1FYoqxEBp3ab3X7I4XuIl-a9fBaUjuFPx/view?usp=sharing

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Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

• Explore the machine learning landscape, particularly neural nets
• Use Scikit-Learn to track an example machine-learning project end-to-end
• Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
• Use the TensorFlow library to build and train neural nets
• Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
• Learn techniques for training and scaling deep neural nets
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Drive link:  https://drive.google.com/file/d/1zRmHJO3QFbMiCLEzr5lMn7pvwCNM7LCn/view?usp=sharing

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Read now thank me later