A basic guide to Machine Learning

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.

The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

Machine Learning for Hackers: Case Studies and Algorithms to Get You Started 1st Edition

by Drew Conway

If you're an experienced programmer interested in crunching data, this book will get you started with machine learning a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.

Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.

Develop a nave Bayesian classifier to determine if an email is spam, based only on its text
Use linear regression to predict the number of page views for the top 1,000 websites
Learn optimization techniques by attempting to break a simple letter cipher
Compare and contrast U.S. Senators statistically, based on their voting records

Machine Learning: An Algorithmic Perspective (Chapman & Hall/CRC Machine Learning & Pattern Recognition) Paperback - Import, 8 Apr 2009

by Stephen Marsland

Traditional books on machine learning can be divided into two groups - those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that makeup machine learning methods but also provides the background needed to understand how and why these algorithms work.

Highlights a Range of Disciplines and Applications

Drawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge.

Machine Learning: The New AI (MIT Press Essential Knowledge series) Kindle Edition

by Ethem Alpaydin

A concise overview of machine learning-computer programs that learn from data-which underlies applications that include recommendation systems, face recognition, and driverless cars.

Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition-as well as some we don't yet use every day, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning-the foundation of efforts to process that data into knowledge-has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.

Real-World Machine Learning 1st Edition

by Henrik Brink

Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you're done, you'll be ready to successfully build, deploy, and maintain your own powerful ML systems.

What's Inside

Predicting future behavior
Performance evaluation and optimization
Analyzing sentiment and making recommendations

Source: HOB

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