Machine Learning

“Available recommendation signals cluster around Social Sciences, ificial, Intelligence, NonFiction, Artificial lists, suggesting this book may fit readers looking for creative discipline, craft, or artistic motivation. Treat this as discovery context, not a quality guarantee.”

“Available recommendation signals cluster around ificial, Intelligence, NonFiction, Artificial, Programming lists, suggesting this book may fit readers looking for big-picture nonfiction and accessible learning. Treat this as discovery context, not a quality guarantee.”

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 pro...

Leverage machine learning to design and backtest automated trading strategies for realworld markets using pandas, TALib, scikitlearn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a researc...

Understand basic to advanced deep learning algorithms, the mathematical principles behind them, and their practical applications. Key Features Get uptospeed with building your own neural networks from scratch Gain insights into the mathematical principles behind deep learning algorithms Implement popular deep learning algorithms such as CNNs, RNN...

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this handson guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How With fastai, the first library to provide a consistent inte...

New edition of the bestselling guide to deep reinforcement learning and how it's used to solve complex realworld problems. Revised and expanded to include multiagent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more Key Features Second edition of the bestselling introduction to deep reinforcement learning, ...

Your nononsense guide to making sense of machine learning Machine learning can be a mindboggling 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, realtime ads on web pages, credit scoring, automation, and email spam fil...

This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problemsolving with real data across a wide variety of applications will aid practitioners who wish to extend their ...

Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this handson book, you'll build an example MLdriven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn the tools, best practice...

The most comprehensive, uptodate introduction to the theory and practice of Artificial Intelligence,.The longanticipated revision of Artificial Intelligence,: A Modern Approach explores the full breadth and depth of the field of Artificial Intelligence, (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in...

“Available recommendation signals cluster around NonFiction, Big, Data, Science, Business lists, suggesting this book may fit readers looking for business judgment, leadership, or practical strategy. Treat this as discovery context, not a quality guarantee.”

Solve realworld data problems with R and machine learning. Key Features Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.5 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with a clear, handson guide by experienced machine ...

This is not a traditional book. The book has a lot of code. If you don't like the code first approach do not buy this book. Making code available on Github is not an option. This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the a...

The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first triedandproven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the experience of hund...

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in realworld data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches...

Featured by Tableau as the first of "7 Books About Machine Learning for Beginners" Ready to crank up a virtual server and smash through petabytes of data Want to add 'Machine Learning' to your LinkedIn profileWell, hold on there...Before you embark on your epic journey into the world of machine learning, there is some theory and statistical princ...

A great building requires a strong foundation. This book teaches basic Artificial Intelligence, algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, Nelder Mead, and linear regression. These are not just foundational algorithms for the rest of the series, but are very useful in their own right. The 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 relevan...

The ability to take raw data, access it, filter it, process it, visualize it, understand it, and communicate it to others is possibly the most essential business problem for the coming decades. "Machine learning," the process of automating tasks once considered the domain of highlytrained analysts and mathematicians, is the key to efficiently extr...

Do you feel that informatics is indispensable in today's increasingly digital world Do you want to introduce yourself to the world of Programming, or cyber security but don't know where to get started If the answer to these questions is yes, then keep reading...This book includes: ...

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 handson case studi...

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for finan...

Forecasting is required in many situations. Deciding whether to build another power generation plant in the next five years requires forecasts of future demand. Scheduling staff in a call centre next week requires forecasts of call volumes. Stocking an inventory requires forecasts of stock requirements. Telecommunication routing requires traffic fo...

Discover valuable machine learning techniques you can understand and apply using just highschool math.In Grokking Machine Learning you will learn: Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex da...

Deep learning is transforming software, facilitating powerful new Artificial Intelligence, capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline's techniques. Packed with fullcolor figures and easytofollow code, it sweeps away the complex...

Engage the world, change the world Deep Learning has claimed the attention of educators and policymakers around the world. This book not only defines what deep learning is, but takes up the question of how to mobilize complex, wholesystem change and transform learning for all students.New Pedagogies for Deep Learning is a global partnership that w...

“Available recommendation signals suggest this title has discovery traction, but there is not enough safe category context to make a stronger reader-fit claim.”

“Available recommendation signals cluster around Artificial, Intelligence lists, suggesting this book may fit readers looking for reader-fit discovery across adjacent interests. Treat this as discovery context, not a quality guarantee.”

SummaryMachine Learning with TensorFlow gives readers a solid foundation in machinelearning concepts plus handson experience coding TensorFlow with Python. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the Technology,TensorFlow, Google's library for largescale machine learning, s...

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a handson, codefirst approach to help you build confidence while you learn key topics.You'll understand how to implement the most common scenarios ...

“Available recommendation signals cluster around Data, Science lists, suggesting this book may fit readers looking for big-picture nonfiction and accessible learning. Treat this as discovery context, not a quality guarantee.”

Artificial Intelligence, is one of the most exciting technologies of the century, and Deep Learning is in many ways the ?brain? behind some of the world?s smartest Artificial Intelligence, systems out there. Loosely based on neuron behavior inside of human brains, these systems are rapidly catching up with the intelligence of their human creators, de...

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine ...

This practical guide provides nearly 200 selfcontained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikitlearn, you'll be able to address specific problems such as loading data, handling text or numerical data, model selection...

For centuries societies?and the policymakers, teachers, and parents that inhabit them?have grappled with the controversy around how students learn best. As far back as Confucius, philosophers and experts have upheld the virtues of learning by doing rather than by listening. Yet U.S. schools continue their attempts to engage students through conven...

Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms.This graduatelevel textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and il...

"If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book."?Cassie Kozyrkov, Chief Decision Scientist at Google"Foundational work about the reality of building machine learning models in production."?Karolis Urbonas, Head of Machine Learning and Science at Amazon...

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.Ideal for programmers, data scient...

“Available recommendation signals suggest this title has discovery traction, but there is not enough safe category context to make a stronger reader-fit claim.”

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 job...

Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do d...

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, ...

Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using handson examples.Deep Learning research is advancing rapidly over the past years. Frameworks and libraries are constantly been developed and updated. However, we still lack standardized solutions on how to serve, deploy and scale Deep Learni...

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement lea...