Over 55 Books on Machine Learning

  Over 55 Books on Machine Learning

Books in list (60)


Title: Understanding Machine Learning

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage. 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 an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed 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 an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.
Author(s): Shai Shalev-Shwartz;Shai Ben-David
ISBN 13: 9781107057135
Pages: 409
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Title: Machine Learning with R Cookbook

If you want to learn how to use R for machine learning and gain insights from your data, then this book is ideal for you.
Author(s): Yu-Wei Chiu
ISBN 13: 9781783982042
Pages: 442
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Title: A First Course in Machine Learning, Second Edition

Introduces the main algorithms and ideas that underpin machine learning techniques and applications. Keeps mathematical prerequisites to a minimum, providing mathematical explanations in comment boxes and highlighting important equations Covers modern machine learning research and techniques. Includes three new chapters on Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models.
Author(s): Simon Rogers;Mark Girolami
ISBN 13: 9781498738484
Pages: 427
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Title: Machine Learning: The New AI (The MIT Press Essential Knowledge series)

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 everyday, 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 the 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. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security.
Author(s): Ethem Alpaydin
ISBN 13: 9780262529518
Pages: 224
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Title: Introduction to Machine Learning with Python

The book covers a machine learning workflow: data preprocessing and working with data, training algorithms, evaluating results, and implementing those algorithms into a production-level system.
Author(s): Sarah Guido
ISBN 13: 9781449369415
Pages: 400
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Title: R Machine Learning By Example

Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully. If you are interested in mining useful information from data using state-of-the-art techniques to make data-driven decisions, this is a go-to guide for you. No prior experience with data science is required, although basic knowledge of R is highly desirable. Prior knowledge in machine learning would be helpful but is not necessary. Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems. This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems. You'll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms. Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.
Author(s): Raghav Bali;Dipanjan Sarkar
ISBN 13: 9781784390846
Pages: 340
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Title: Machine Learning: Hands-On for Developers and Technical Professionals

Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: Learn the languages of machine learning including Hadoop, Mahout, and Weka Understand decision trees, Bayesian networks, and artificial neural networks Implement Association Rule, Real Time, and Batch learning Develop a strategic plan for safe, effective, and efficient machine learning By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.
Author(s): Jason Bell
ISBN 13: 9781118889060
Pages: 408
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Title: Machine Learning Refined: Foundations, Algorithms, and Applications

Providing a unique approach to machine learning, this text contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts necessary to conduct research, build products, tinker, and play. By prioritizing geometric intuition, algorithmic thinking, and practical real world applications in disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology, this text provides readers with both a lucid understanding of foundational material as well as the practical tools needed to solve real-world problems. With in-depth Python and MATLAB/OCTAVE-based computational exercises and a complete treatment of cutting edge numerical optimization techniques, this is an essential resource for students and an ideal reference for researchers and practitioners working in machine learning, computer science, electrical engineering, signal processing, and numerical optimization.
Author(s): Jeremy Watt;Reza Borhani;Aggelos Katsaggelos
ISBN 13: 9781107123526
Pages: 300
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Title: Foundations of Machine Learning (Adaptive Computation and Machine Learning series)

This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book. The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.
Author(s): Mehryar Mohri
ISBN 13: 9780262018258
Pages: 432
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Title: A Collection of Advanced Data Science and Machine Learning Interview Questions Solved in Python and Spark (Ii)

A collection of Machine Learning interview questions in Python and Spark
Author(s): Antonio Gulli
ISBN 13: 9781518678646
Pages: 106
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Title: Machine Learning For Dummies

It may sound a bit intimidating, but machine learning is an exciting new way to teach your computer to perform all sorts of important and useful tasks. This book is the easy way to get up to speed. Your no-nonsense guide to making sense of machine learning Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly.
Author(s): John Paul Mueller;Luca Massaron
ISBN 13: 9781119245513
Pages: 432
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Title: Fundamentals of Machine Learning for Predictive Data Analytics

A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
Author(s): John D. Kelleher;Brian Mac Namee;Aoife DArcy
ISBN 13: 9780262029445
Pages: 624
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Title: The Master Algorithm

The Master Algorithm will not be limited to solving particular problems but will be able to learn anything and solve any problem, however difficult, and Pedro Domingos, a trailblazing computer scientist, is at the very forefront of the ...
Author(s): Pedro Domingos
ISBN 13: 9780465065707
Pages: 352
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Title: Pattern Recognition and Machine Learning

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Author(s): Christopher M. Bishop
ISBN 13: 9780387310732
Pages: 760
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Title: Python Machine Learning

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics. If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
Author(s): Sebastian Raschka
ISBN 13: 9781783555130
Pages: 454
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Title: Machine Learning for Healthcare

This book focuses on the tools of machine learning and statistics in a practical manner, with lots of case studies specific to the challenges of working with healthcare data. As storage and collection technology has become cheaper and more precise, companies and individuals are eager to extract relevant information from large data sets. This book focuses on the tools of machine learning and statistics in a practical manner, with lots of case studies specific to the challenges of working with healthcare data. By exploring each problem in depth, you’ll build your intuitive understanding of machine learning without requiring a strong background in advanced mathematics. You’ll be able to recognize when your problems match traditional problems closely, and apply classical tools from statistics to your problems, while working within the legal bounds of the US healthcare system.
Author(s): John Schrom
ISBN 13: 9781491947005
Pages: 300
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Title: Machine Learning with R - Second Edition

Harness the power of R for statistical computing and data science. Explore, forecast, and classify data with R. Use R to apply common machine learning algorithms to real-world scenarios. Harness the power of R to build common machine learning algorithms with real-world data science applications. Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results. Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems. Classify your data with Bayesian and nearest neighbour methods. Predict values by using R to build decision trees, rules, and support vector machines. Forecast numeric values with linear regression, and model your data with neural networks. Evaluate and improve the performance of machine learning models. Learn specialized machine learning techniques for text mining, social network data, big data, and more.
Author(s): Brett Lantz
ISBN 13: 9781784393908
Pages: 454
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Title: Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

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. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Author(s): Kevin P. Murphy
ISBN 13: 9780262018029
Pages: 1104
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Title: Machine Learning

This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques - together with the Bayesian inference approach, whose essence lies ...
Author(s): Sergios Theodoridis
ISBN 13: 9780128015223
Pages: 1062
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Title: Data Science from Scratch: First Principles with Python 1st Edition

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.
Author(s): Joel Grus
ISBN 13: 9781491901427
Pages: 330
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Title: Bayesian Reasoning and Machine Learning

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.
Author(s): David Barber
ISBN 13: 9780521518147
Pages: 735
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Title: Make Your Own Neural Network

A gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.
Author(s): Tariq Rashid
ISBN 13: 9781530826605
Pages: 222
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Title: Fundamentals of Deep Learning

This book uses exposition and examples to help you understand major concepts in this complicated field. Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing in-house deep learning teams.
Author(s): Nikhil Buduma
ISBN 13: 9781491925614
Pages: 150
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Title: Deep Learning

If you work in the embedded, desktop, and big data/Hadoop spaces and really want to understand deep learning, this is your book. Looking for one central source where you can learn key findings on machine learning? Deep Learning: The Definitive Guide provides developers and data scientists with the most practical information available on the subject, including deep learning theory, best practices, and use cases. Authors Adam Gibson and Josh Patterson present the latest relevant papers and techniques in a non­academic manner, and implement the core mathematics in their DL4J library. If you work in the embedded, desktop, and big data/Hadoop spaces and really want to understand deep learning, this is your book.
Author(s): Adam Gibson;Josh Patterson
ISBN 13: 9781491914250
Pages: 200
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Title: Building Machine Learning Systems with Python

This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. As the Big Data explosion continues at an almost incomprehensible rate, being able to understand and process it becomes even more challenging. With Building Machine Learning Systems with Python, you'll learn everything you need to tackle the modern data deluge - by harnessing the unique capabilities of Python and its extensive range of numerical and scientific libraries, you will be able to create complex algorithms that can 'learn' from data, allowing you to uncover patterns, make predictions, and gain a more in-depth understanding of your data. Featuring a wealth of real-world examples, this book provides gives you with an accessible route into Python machine learning. Learn the Iris dataset, find out how to build complex classifiers, and get to grips with clustering through practical examples that deliver complex ideas with clarity. Dig deeper into machine learning, and discover guidance on classification and regression, with practical machine learning projects outlining effective strategies for sentiment analysis and basket analysis. The book also takes you through the latest in computer vision, demonstrating how image processing can be used for pattern recognition, as well as showing you how to get a clearer picture of your data and trends by using dimensionality reduction.
Author(s): Willi Richert;Luis Pedro Coelho
ISBN 13: 9781782161400
Pages: 290
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Title: Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
Author(s): Richard S. Sutton;Andrew G. Barto
ISBN 13: 9780262193986
Pages: 322
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Title: Artificial Intelligence for Humans, Volume 3

In this book, we will demonstrate the neural networks in a variety of real-world tasks such as image recognition and data science.
Author(s): Jeff Heaton
ISBN 13: 9781505714340
Pages: 374
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Title: Machine Learning with Spark - Tackle Big Data with Powerful Spark Machine Learning Algorithms

Apache Spark is a framework for distributed computing that is designed from the ground up to be optimized for low latency tasks and in-memory data storage. It is one of the few frameworks for parallel computing that combines speed, scalability, in-memory processing, and fault tolerance with ease of programming and a flexible, expressive, and powerful API design. This book guides you through the basics of Spark's API used to load and process data and prepare the data to use as input to the various machine learning models. There are detailed examples and real-world use cases for you to explore common machine learning models including recommender systems, classification, regression, clustering, and dimensionality reduction. You will cover advanced topics such as working with large-scale text data, and methods for online machine learning and model evaluation using Spark Streaming.
Author(s): Nick Pentreath
ISBN 13: 9781783288519
Pages: 338
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Title: Machine Learning in Java

If you want to learn how to use Java's machine learning libraries to gain insight from your data, this book is for you. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications in real life. You should be familiar with Java programming and data mining concepts to make the most of this book, but no prior experience with data mining packages is necessary.
Author(s): Bostjan Kaluza
ISBN 13: 9781784396589
Pages: 258
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Title: Numerical Algorithms

The book covers a wide range of topics--from numerical linear algebra to optimization and differential equations--focusing on real-world motivation and unifying themes.
Author(s): Justin Solomon
ISBN 13: 9781482251883
Pages: 392
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Title: Machine Learning: The Art and Science of Algorithms that Make Sense of Data

As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

Author(s): Peter Flach
ISBN 13: 9781107422223
Pages: 416
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Title: Python Machine Learning

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics. If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
Author(s): Sebastian Raschka
ISBN 13: 9781783555130
Pages: 454
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Title: An Introduction to Machine Learning

This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications.
Author(s): Miroslav Kubat
ISBN 13: 9783319200095
Pages: 291
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Title: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition - 2nd Edition

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—-the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for &'grave;wide'' data (p bigger than n), including multiple testing and false discovery rates.

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

Author(s): Trevor Hastie;Robert Tibshirani;Jerome Friedman
ISBN 13: 9780387848570
Pages: 768
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Title: Machine Learning in Python

This book shows readers how they can successfully analyze data using only two core machine learning algorithms---and how to do so using the popular Python programming language.
Author(s): Michael Bowles
ISBN 13: 9781118961742
Pages: 336
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Title: Machine Learning in Action

Machine Learning in Action is 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. A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.
Author(s): Peter Harrington
ISBN 13: 9781617290183
Pages: 384
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Title: An Introduction to Statistical Learning

This book presents some of the most important modeling and prediction techniques, along with relevant applications.
Author(s): Gareth James;Daniela Witten;Trevor Hastie;Robert Tibshirani
ISBN 13: 9781461471370
Pages: 426
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Title: Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments

This book serves two purposes. First, it teaches the importance of using sophisticated yet accessible statistical methods to evaluate a trading system before it is put to real-world use. In order to accommodate readers having limited mathematical background, these techniques are illustrated with step-by-step examples using actual market data, and all examples are explained in plain language. Second, this book shows how the free program TSSB (Trading System Synthesis & Boosting) can be used to develop and test trading systems. The machine learning and statistical algorithms available in TSSB go far beyond those available in other off-the-shelf development software. Intelligent use of these state-of-the-art techniques greatly improves the likelihood of obtaining a trading system whose impressive backtest results continue when the system is put to use in a trading account. Among other things, this book will teach the reader how to: Estimate future performance with rigorous algorithms Evaluate the influence of good luck in backtests Detect overfitting before deploying your system Estimate performance bias due to model fitting and selection of seemingly superior systems Use state-of-the-art ensembles of models to form consensus trade decisions Build optimal portfolios of trading systems and rigorously test their expected performance Search thousands of markets to find subsets that are especially predictable Create trading systems that specialize in specific market regimes such as trending/flat or high/low volatility More information on the TSSB program can be found at TSSBsoftware dot com.
Author(s): David Aronson;Timothy Masters
ISBN 13: 9781489507716
Pages: 520
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Title: Port Security Management

This book provides a comprehensive text on the geotechnical and geological aspects of the investigations for and the design and construction of new dams and the review and assessment of existing dams.
Author(s): Kenneth Christopher
ISBN 13: 9781466583283
Pages: 336
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Title: Machine Learning with R

Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly.
Author(s): Brett Lantz
ISBN 13: 9781782162148
Pages: 396
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Title: Real-World Machine Learning

In a world where big data is the norm and near-real-time decisions are crucial, machine learning (ML) is a critical component of the data workflow. Machine learning systems can quickly crunch massive amounts of information to offer insights and make decisions in a way that matches or even surpasses human cognitive abilities. These systems use sophisticated computational and statistical tools to build models that can recognize and visualize patterns, predict outcomes, forecast values, and make recommendations. Real-World Machine Learning is a practical guide designed to teach developers the art of ML project execution. The book introduces the day-to-day practice of machine learning and prepares readers to successfully build and deploy powerful ML systems. Using the Python language and the R statistical package, it starts with core concepts like data acquisition and modeling, classification, and regression. Then it moves through the most important ML tasks, like model validation, optimization and feature engineering. It uses real-world examples that help readers anticipate and overcome common pitfalls. Along the way, they will discover scalable and online algorithms for large and streaming data sets. Advanced readers will appreciate the in-depth discussion of enhanced ML systems through advanced data exploration and pre-processing methods. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Author(s): Henrik Brink;Joseph Richards;Mark Fetherolf
ISBN 13: 9781617291920
Pages: 400
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Title: Introduction to Machine Learning

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already,including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory,parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments;case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming,probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.

Author(s): Ethem Alpaydin
ISBN 13: 9780262012430
Pages: 584
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Title: Neural Networks and Learning Machines (3rd Edition)

The third edition of this classic book presents a comprehensive treatment of neural networks and learning machines. The book has been revised extensively to provide an up-to-date treatment of the subject.
Author(s): Simon S Haykin
ISBN 13: 9788120340008
Pages: 936
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Title: Machine Learning

This exciting addition to the McGraw-Hill Series in Computer Science focuses on the concepts and techniques that contribute to the rapidly changing field of machine learning—including probability and statistics, artificial intelligence, and neural networks—unifying them all in a logical and coherent manner. Machine Learning serves as a useful reference tool for software developers and researchers, as well as an outstanding text for college students.

Author(s): Tom M. Mitchell
ISBN 13: 9780070428072
Pages: 432
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Title: Data Mining: Practical Machine Learning Tools and Techniques

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.

Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.

*Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Author(s): Ian H. Witten
ISBN 13: 9780123748560
Pages: 664
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Title: Practical Machine Learning

Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniquesAbout This Book- Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and ...
Author(s): Sunila Gollapudi
ISBN 13: 9781784399689
Pages: 468
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Title: Advanced Analytics with Spark: Patterns for Learning from Data at Scale

In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection among others—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find these patterns useful for working on your own data applications.
Author(s): Sandy Ryza;Uri Laserson;Sean Owen;Josh Wills
ISBN 13: 9781491912768
Pages: 276
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Title: Learning from Data

This book, together with specially prepared online material freely accessible to our readers, provides a complete introduction to Machine Learning, the technology that enables computational systems to adaptively improve their performance with experience accumulated from the observed data. Such techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. In addition, our readers are given free access to online e-Chapters that we update with the current trends in Machine Learning, such as deep learning and support vector machines. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. What we have emphasized are the necessary fundamentals that give any student of learning from data a solid foundation. The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.
Author(s): Yaser S. Abu-Mostafa;Malik Magdon-Ismail;Hsuan-Tien Lin
ISBN 13: 9781600490064
Pages: 201
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Title: Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners

With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders.
Author(s): Jared Dean
ISBN 13: 9781118618042
Pages: 265
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Title: Storytelling with Data

This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story.
Author(s): Cole Nussbaumer Knaflic
ISBN 13: 9781119002253
Pages: 384
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Title: Machine Learning

Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.
Author(s): Mohssen Mohammed;Muhammad Badruddin Khan;Ejhab Bashier Mohammed Bashier
ISBN 13: 9781498705387
Pages: 168
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Title: Machine Learning Projects for .NET Developers

While some machine learning algorithms use fairly advanced mathematics, this book focuses on simple but effective approaches. If you enjoy hacking code and data, this book is for you.
Author(s): Mathias Brandewinder
ISBN 13: 9781430267676
Pages: 320
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Title: Large Scale Machine Learning with Python

This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful.
Author(s): Bastiaan Sjardin;Luca Massaron;Alberto Boschetti
ISBN 13: 9781785887215
Pages: 439
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Title: Introduction to Statistical Machine Learning

Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice.
Author(s): Masashi Sugiyama
ISBN 13: 9780128021217
Pages: 534
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Title: Javascript Artificial Intelligence: Made Easy

Design the MIND of a Robotic Thinker! “ This book will help you get started with this exciting language and gives you an idea of what is possible. “ - Melchizedek B, from Amazon.com “ The examples it uses are easy to follow and the illustrations bring out the more complex aspects while making them simple. “ - C. Brant, from Amazon.com “ Such a cool book that covers basic Javascript programming then incorporates tools and components to explore Artificial Intelligence. “ - M. Gavel, from Amazon.com * * INCLUDED BONUS: a Quick-start guide to Learning Javascript in less than a Day! * * How would you like to Create the Next SIRI? Artificial Intelligence. One of the most brilliant creations of mankind. No longer a sci-fi fantasy, but a realistic approach to making work more efficient and lives easier. And the best news? It’s not that complicated after all Does it require THAT much advanced math? NO! And are you paying THOUSANDS of dollars just to learn this information? NO! Hundreds? Not even close. Within this book's pages, you'll find GREAT coding skills to learn - and more. Just some of the questions and topics include: - Complicated scheduling problem? Here’s how to solve it. - How good are your AI algorithms? Analysis for Efficiency - How to interpret a system into logical code for the AI - How would an AI system would diagnose a system? We show you... - Getting an AI agent to solve problems for you and Much, much more! World-Class Training This book breaks your training down into easy-to-understand modules. It starts from the very essentials of algorithms and program procedures, so you can write great code - even as a beginner!
Author(s): Code Well Academy
ISBN 13: 9781530826872
Pages: 156
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Title: Advanced Machine Learning with Python

This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful.
Author(s): John Hearty
ISBN 13: 9781784398637
Pages: 266
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Title: Machine Learning for the Web

Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and mediumsized data sets. This is a unique book that helps bridge the gap between machine learning and web development. It focuses on the difficulties of implementing predictive analytics in web applications. We focus on the Python language, frameworks, tools, and libraries, showing you how to build a machine learning system. You will explore the core machine learning concepts and then develop and deploy the data into a web application using the Django framework. You will also learn to carry out web, document, and server mining tasks, and build recommendation engines. Later, you will explore Python's impressive Django framework and will find out how to build a modern simple web app with machine learning features.
Author(s): Andrea Isoni
ISBN 13: 9781785886607
Pages: 285
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Title: Machine Learning for Hackers

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 naïve 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
  • Build a “whom to follow” recommendation system from Twitter data
Author(s): Drew Conway
ISBN 13: 9781449303716
Pages: 324
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Title: Machine Learning and Data Science

This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised ...
Author(s): Daniel D. Gutierrez
ISBN 13: 9781634620963
Pages: 230
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Title: Introduction To Machine Learning 3Rd Edition

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.
Author(s): Ethem Alpaydin
ISBN 13: 9788120350786
Pages: 640
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