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Master the new features in PySpark 3.1 to develop data-driven, intelligent applications. This updated edition covers topics ranging from building scalable machine learning models, to natural language processing, to recommender systems.Machine Learning with PySpark, Second Edition begins with the fundamentals of Apache Spark, including the...

TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine.Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concept...

Python Machine Learning By Example, 3rd Edition serves as a comprehensive gateway into the world of machine learning (ML).With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing...

Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you'll dive into the more specific fields of machine learnin...

Machine learning has great potential for improving products, processes and research.But computers usually do not explain their predictions which is a barrier to the adoption of machine learning.This book is about making machine learning models and their decisions interpretable.

After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression.The focus of the book is on model-agnostic methods for interpreting black box models such as feature importance and accumulated local effects, and explaining individual predictions with Shapley values and LIME.In addition, the book presents methods specific to deep neural networks.

All interpretation methods are explained in depth and discussed critically.How do they work under the hood?What are their strengths and weaknesses?How can their outputs be interpreted?This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.

With this book, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering.

The Deep Learning textbook is a resource intended to help studentsand practitioners enter the field of machine learning in generaland deep learning in particular.The online version of the book is now complete and will remainavailable online for free.

If you notice any typos (besides the known issues listed below) or have suggestions for exercises to add to thewebsite, do not hesitate to contact the authors directly by e-mailat: feedback@deeplearningbook.org

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing 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 colour 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.

An Introduction to Statistical Learning, with Applications in R, written by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, is an absolute classic in the space. The book, a staple of statistical learning texts, is accessible to readers of all levels, and can be read without much of an existing foundational knowledge in the area.

As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. Each chapter includes an R lab. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

"An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. The authors give precise, practical explanations of what methods are available, and when to use them, including explicit R code. Anyone who wants to intelligently analyze complex data should own this book."

The go-to bible for this data scientist and many others is The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Each of the authors is an expert in machine learning / prediction, and in some cases invented the techniques we turn to today to make sense of big data: ensemble learning methods, penalized regression, additive models and nonparemetric smoothing, and much much more.

In 2009, the second edition of the book added new chapters on random forests, ensemble learning, undirected graphical models, and high dimensional problems. And now, thanks to an agreement between the authors and the publisher, a PDF version of the 2nd edition is now available for free download.

Avoiding False Discoveries: A completely new addition in the second edition is a chapter on how to avoid false discoveries and produce valid results, which is novel among other contemporary textbooks on data mining. It supplements the discussions in the other chapters with a discussion of the statistical concepts (statistical significance, p-values, false discovery rate, permutation testing, etc.) relevant to avoiding spurious results, and then illustrates these concepts in the context of data mining techniques. This chapter addresses the increasing concern over the validity and reproducibility of results obtained from data analysis. The addition of this chapter is a recognition of the importance of this topic and an acknowledgment that a deeper understanding of this area is needed for those analyzing data.

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 while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.

Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

In particular, R has literally thousands of downloadable add-onpackages, many of which implement alternative algorithms and statisticalmethods. This book concentrates on the core functionality availablethrough the basic distribution combined with several important packages knowncollectively as the tidyverse.

The base distribution of R has frequent and planned releases, but thelanguage definition and core implementation are stable. The recipes inthis book should work with any recent release of the base distribution.

This book introduces the popular, powerful and free programming language and software package R with a focus on the implementation of standard tools and methods used in econometrics. Unlike other books on similar topics, it does not attempt to provide a self-contained discussion of econometric models and methods. Instead, it builds on the excellent and popular textbook "Introductory Econometrics" by Jeffrey M. Wooldridge. Some other editions and versions work as well, see below. It is compatible in terms of topics, organization, terminology and notation, and is designed for a seamless transition from theory to practice. Topics include:

The new Section 1.5 introduces the concepts of the "tidyverse". This set of packages offers a convenient, powerful, and recently very popular approach to data manipulation and visualization. Knowledge of the tidyverse is not required for the remainder of the book but very useful for working with real world data. Section 1.3.6 on data import and export has been updated. It now stresses the use of the packages haven and rio which are newer and for most applications both more powerful and more convenient than the approaches presented in the first edition. There is a new R package "wooldridge" by Justin M. Shea and Kennth H. Brown. It very conveniently provides all example data sets. All example R scripts have been updated to use this package instead of loading the data from a data file. When discussing financial time series data in Section 10.2, the second edition now uses the "quantmod" instead of the "pdfetch" package. An introduction of ANOVA tables has been added in Sections 6.1.5, 7.3, and 7.4. Various smaller additions and updates are added and numerous errors, typos, and unclear explanations have been fixed. 2b1af7f3a8