Find a way out to learn Machine Learning Algorithms from these Books

Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide.
Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation.
Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide.

Book Description

As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge.

Information Theory, Inference, and Learning Algorithms Hardcover - 25 Sep 2003

by David J. C. MacKay

Information theory and inference taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R Kindle Edition

by V Kishore Ayyadevara

Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R.

You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence.

What You Will Learn

Get an in-depth understanding of all the major machine learning and deep learning algorithms
Fully appreciate the pitfalls to avoid while building models
Implement machine learning algorithms in the cloud
Follow a hands-on approach through case studies for each algorithm
Gain the tricks of ensemble learning to build more accurate models
Discover the basics of programming in R/Python and the Keras framework for deep learning

Understanding Machine Learning: From Theory to Algorithms Paperback - 2015

by Shai Shalev-Shwartz

Machine learning is one of the fastest-growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

Source: HOB

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