Machine Learning
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An Introduction to Machine Learning
Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. Offers a comprehensive introduction to Machine Learning, while not assuming any priorknowledge of the topic; Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning; Not tied to any specific software language or hardware implementation.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Author: Aurélien Géron
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2019-09-05
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
Machine Learning
This book attempts to provide a unified overview of the broad field of Machine Learning and its Practical implementation. This book is a survey of the state of art. It breaks this massive subject into comprehensible parts piece by piece. The objective is to focus on basic principles of machine learning with some leading edge topics. This book addresses a full spectrum of machine learning programming. The emphasis is to solve lot many programming examples using step-by step practical implementation of machine learning algorithms. To facilitate easy understanding of machine learning, this book has been written in such a simple style that a student thinks as if a teacher is sitting behind him and guiding him. This book is written as per the new syllabus of different Universities of India. It also Cover the syllabus of B.Tech.(CSE/IT), MCA, BCA of Delhi University, Delhi. GGSIPU, MDU, RGTU, Nagpur University, UTU, APJ Abdul Kalam University so on. The book is intended for both academic and professional audience.
