AI Books

On this page we highlight some books that you might find worthwhile to read. If you know of any other books that we should highlight on the MIIA website or via our real-time community messaging platform (MIIA on Slack), please let us know either on Slack or via info@machineintelligenceafrica.org.   

The Future of Machine Intelligence, Perspectives from Leading PractitionersBy David BeyerPublisher: O’ReillyReleased: March 2016

Advances in both theory and practice are throwing the promise of machine learning into sharp relief. The field has the potential to transform a range of industries, from self-driving cars to intelligent business applications. Yet machine learning is so complex and wide-ranging that even its definition can change from one person to the next.


Herewith a list of recommended books covering Data Analysis, Data Science, Machine Learning, Data Visualization, Statistics & Associated Programming Languages from Data Science Weekly

Data Scientists at Work 
A collection of interviews with 16 of the world’s most influential and innovative data scientists from across the spectrum of this hot new profession – from Yann LeCun at Facebook, to Daniel Tunkelang at LinkedIn, to Caitlin Smallwood at Netflix, to Jake Porway at DataKind and more …

General

 

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Street-Fighting Mathematics: The Art of Educated Guessing and Opportunistic Problem Solving 

 

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Data Driven: Profiting from Your Most Important Business Asset 

 

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Competing on Analytics: The New Science of Winning 

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Data Analysis with Open Source Tools 

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Data Source Handbook 

 

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Who’s #1?: The Science of Rating and Ranking 


Data Science

 

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Doing Data Science: Straight Talk from the Frontline 

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Data Smart: Using Data Science to Transform Information into Insight 

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Data Science for Business: 
What you need to know about data mining and data-analytic thinking
 


Statistics

 

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An Introduction to Statistical Learning: with Applications in R 

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Data Analysis Using Regression and Multilevel/Hierarchical Models 

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Statistics As Principled Argument 

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A Handbook of Statistical Analyses Using R, Second Edition 

 

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Mathematical Statistics and Data Analysis (with CD Data Sets) 


Machine Learning

 

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Pattern Recognition and Machine Learning 

 

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Bayesian Reasoning and Machine Learning 

 

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Machine Learning: A Probabilistic Perspective 

 

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The LION Way: Learning plus Intelligent Optimization 


Natural Language Processing (NLP)

 

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Speech and Language Processing, 2nd Edition 

 

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Foundations of Statistical Natural Language Processing 

 

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Natural Language Processing with Python 

 

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Graph-based Natural Language Processing and Information Retrieval 

 

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Natural Language Processing for Online Applications: Text retrieval, extraction and categorization. Second revised edition 


Data Visualization

 

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Visualizing Data: Exploring & Explaining Data with Processing Environment 

 

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The Visual Display of Quantitative Information 


Data Mining

 

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Mining of Massive Datasets 

 

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Data-Intensive Text Processing with MapReduce 

 

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Data Mining with R: Learning with Case Studies 


R

 

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The Art of R Programming: A Tour of Statistical Software Design 

 

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R Graphics Cookbook 


Python

 

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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 

 

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Learning Python, 5th Edition 


Spark

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Learning Spark: Lightning-fast big data analytics 

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Fast Data Processing with Spark 


Big Data Tools

 

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Hadoop: The Definitive Guide 

 

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Programming Pig 

 

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Practical Cassandra: A Developer’s Approach


Herewith a list of recommended books in a blog post on Data Science Central

1. Overviews and theories – the ideas behind the Big Data revolution, mostly written for any audience regardless of technical ability.

The Human Face of Big Data, created by Rick Smolan and Jennifer Erwitt

Rather than a formulaic textbook, this book talks the reader through the ideas and applications of Big Data through a series of essays and photographs. It pays particular attention to humanizing the story – showing how the technologies being discussed are affecting the lives of real people around the world. The essays come from a range of authors noted for their thoughts on the impact of technology and data on society.

Big Data: A Revolution that will Transform how we Live, Work and Think

By Viktor Mayer-Schonberger and Kenneth Cukier.

http://www.amazon.com/Big-Data-Revolution-Transform-Think/dp/054422…

This book aims to examine the social impact of the ever-growing amount of data we are collecting, storing and analyzing, as well as providing the reader with a practical toolkit for surviving and thriving in a Big Data world.

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die

By Eric Siegel

http://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1…

Referred to as “The Freakonomics of Big Data”, this book is written for any audience regardless of technical expertise and explores the many ways in which data analysis seems to be giving us the change to predict, and therefore change, the future. Author Siegel is the founder and editor of the Predictive Analytics Times.

Pattern Recognition and Machine Learning

By Christopher Bishop

http://www.amazon.com/Pattern-Recognition-Learning-Information-Stat…

This book assumes no prior knowledge of the subject matter, but readers with some intermediate knowledge of mathematics, such as linear algebra and calculus will find it easier going than those without. It explains and illustrates the way data scientists are introducing Bayesian algorithms to enable computers to make decisions more quickly and reliably than any human ever could.

Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World 1st Edition 

By Bruce Schneier

http://www.amazon.com/Data-Goliath-Battles-Collect-Control/dp/03932…

Every day we are being watched and recorded, by governments as well as corporations, hell-bent on collecting as much information about us as they can. But why? What do they want? And, how can we make sure that the benefits we gain from living in an increasingly digitized and data-centred world outweigh the freedom and anonymity we are sacrificing? This book provides answers to these questions.

Smart Cities – Big Data, Civic Hackers, and the Quest for a New Utopia

by Anthony M. Townsend

An examination of how datafication of urban spaces and services is changing the way we live in cities, and how what we are seeing start to happen now – in cities such as Chicago, Zaragoza, Spain, and Milton Keynes, UK, is only the beginning.

2. Practical use – Books which explain specific technical skills, not always suited to beginners

Hadoop, the Definitive Guide

By Tom White

The elephant in the room that everyone is talking about. This practical guide to Hadoop is aimed at programmers and data scientists who want to get started using the Hadoop distributed Big Data framework for analytics and predictive modelling.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

By Trevor Hastie, Robert Tibshirani, Jerome Friedman

http://www.amazon.com/Elements-Statistical-Learning-Prediction-Stat…

This is a great book which looks a little deeper into the science behind the theories. You won’t need a maths degree but it goes into some depth on the statistical theories and concepts behind machine learning and predictive algorithms.

MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems

By Donald Miner

http://www.amazon.com/MapReduce-Design-Patterns-Effective-Algorithm…

An overview, along with example code, of building MapReduce patterns for use in Big Data and analytical projects. The book was written with the aim of bringing all the disparate information on the subject together from the academic research papers, online communities and blogs where it has evolved.

Python for Data Analysis

By Wes McKinney

http://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/144…

There are lots of free courses online which can teach you Python, but as mentioned in the intro, you sometimes just can’t beat a well written and structured book. Python is one of the most popular programming languages for handling data and creating predictive algorithms, and this book explains in detail how to apply it to Big Data tasks.

Practical Data Science with R

By Nina Zumel and John Mount

https://www.manning.com/books/practical-data-science-with-r

The basic principles along with real-world case studies showing the many applications of R in statistical modelling and predictive analytics. Not for total R beginners – the emphasis is on explaining how the language can be applied to creating algorithms for data analysis, rather than teaching a beginner to code in R, but most people with a basic understanding of computer programming principles should be able to follow it.

3. Miscellaneous – books covering the dark side of Big Data, hobbyist applications and specific applications.

Future Crimes

By Marc Goodman

http://www.amazon.com/Future-Crimes-Everything-Connected-Vulnerable…

If you have difficulty sleeping due to thoughts of burglars analyzing social media to determine the best time to break into your house, or hacking your baby monitor to spy on your family, you might want to give this one a miss. An examination of the many ways criminals are taking advantage of our always-connected society.

Internet of Things – Home Projects for Raspberry Pi, Arduino and Beaglebones Black

By Donald Norris

http://www.amazon.com/Internet-Things-Do—Yourself-BeagleBone/dp/0…

Fancy having a go at building your own IOT home lighting, security or environmental control system? This book will show you how to put together the hardware using cheap microcontrollers and off-the-shelf components, and explain the programming needed to make it all work.

Building Data Science Teams

By DJ Patil

http://www.amazon.com/Building-Data-Science-Teams-Patil-ebook/dp/B0…

Written by the US Chief Data Scientist and currently a free ebook download at Amazon, this book looks at the mix of skills business leaders need to harness to make the most of analytics in their organizations.

Visualize This: The FlowingData Guide to Design, Visualization, and Statistics 1st Edition

By Nathan Yau

http://www.amazon.com/Visualize-This-FlowingData-Visualization-Stat…

Explains the principles of visual storytelling with Big Data. How to set goals regarding what you need to explain and what is just noise, and creatively express your results in a way that will get the attention of your intended audience .


Herewith a list of recommended books from Amazon:

  •  Data Science from Scratch: First Principles with PythonApr 30, 2015 
  •   What Is Data Science?Apr 10, 2012 
  •   Data Science for Business: What You Need to Know about Data Mining and Data-Analytic ThinkingAug 19, 2013  
  •   Data Smart: Using Data Science to Transform Information into InsightNov 4, 2013 
  •   Data Science For DummiesMar 9, 2015  
  •   Storytelling with Data: A Data Visualization Guide for Business ProfessionalsNov 2, 2015  
  •   Data Analytics Made AccessibleMay 1, 2014  
  •   Data DrivenJan 29, 2015  
  •   Python Machine LearningSep 23, 2015  
  •   Naked Statistics: Stripping the Dread from the DataJan 13, 2014  
  •   The Data Science Handbook: Advice and Insights from 25 Amazing Data ScientistsJun 19, 2015  
  •   Machine Learning: The Art and Science of Algorithms that Make Sense of DataNov 12, 2012  
  •   Practical Data Science with RApr 13, 2014  
  •   R for Data ScienceSep 25, 2016  
  •   How Data Science Is Transforming Health CareAug 31, 2012  
  •   Introducing Data Science: Big Data, Machine Learning, and more, using Python toolsMay 23, 2016  
  •   Big Data MBA: Driving Business Strategies with Data ScienceDec 21, 2015 
  •   Building Data Science TeamsSep 15, 2011  
  •   Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data Jan 27, 2015  
  •   Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPythonNov 1, 2012  
  •   Hadoop ExplainedJun 16, 2014  
  •   Data Science Interviews ExposedMay 25, 2015
  •   Predictive Analytics For DummiesMar 24, 2014  
  •   An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)Aug 12, 2013  
  •   Analytics: Data Science, Data Analysis and Predictive Analytics for BusinessFeb 19, 2016 
  •   Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or DieJan 11, 2016 
  •   Big Data Science & Analytics: A Hands-On ApproachApr 15, 2016  
  •   Mastering Python for Data ScienceAug 31, 2015  
  •   Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies…Jul 24, 2015  
  •   Learning to Love Data ScienceNov 19, 2015  
  •   Python for Data Science For Dummies (For Dummies (Computers))Jul 7, 2015  
  •   Data Science in Higher Education: A Step-by-Step Introduction to Machine Learning for Institutional ResearchersSep 6, 2015  
  •   Data Science in Python. Volume 3: Plots and Charts with Matplotlib, Data Analysis with Python and SQLiteApr 25, 2016  
  •   Analytics in a Big Data World: The Essential Guide to Data Science and its Applications (Wiley and SAS Business…May 19, 2014  
  •   Statistics: The Art and Science of Learning from Data (3rd Edition)Jan 6, 2012  
  •   Deep Learning: A Practitioner’s ApproachDec 25, 2016  
  •   Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science (FT Press Analytics)Oct 11, 2014
  • Data Science at the Command Line: Facing the Future with Time-Tested ToolsOct 12, 2014 
  •   Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving (Chapman & Hall/CRC…Apr 21, 2015 
  •   Big Data For Beginners: Understanding SMART Big Data, Data Mining & Data Analytics For improved Business Performance…Feb 27, 2016  
  •   Getting Started with Data Science: Making Sense of Data with Analytics (IBM Press)Dec 23, 2015  
  •   A collection of Data Science Interview Questions Solved in Python and Spark: Hands-on Big Data and Machine Learning…Sep 22, 2015 
  •   Cracking the Coding Interview, 6th Edition: 189 Programming Questions and SolutionsJul 1, 2015  
  •   Python Data Science Handbook: Tools and Techniques for DevelopersOct 25, 2016  
  •   Big Data: Principles and best practices of scalable realtime data systemsMay 10, 2015  
  •   Data Jujitsu: The Art of Turning Data into ProductJul 19, 2012 
  • A collection of Advanced Data Science and Machine Learning Interview Questions Solved in Python and Spark (II…Nov 18, 2015  
  •   Sports Analytics and Data Science: Winning the Game with Methods and Models (FT Press Analytics)Dec 2, 2015  
  •   Bayesian Data Analysis, Third Edition (Chapman & Hall/CRC Texts in Statistical Science)Nov 1, 2013  
  •   Data Mining For DummiesSep 29, 2014 
  •   Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and MoreOct 20, 2013  
  •   Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (Morgan Kaufmann Series in Data Management…Jan 6, 2011  
  •   Data Wrangling with Python: Tips and Tools to Make Your Life EasierFeb 20, 2016  
  •   R: Bootcamp – Learn The Basics of Ruby Programming in 2 Weeks! (FREE Bonus, R Programming For Data Science)Mar 31, 2016 
  •   Automate the Boring Stuff with Python: Practical Programming for Total BeginnersMay 1, 2015 
  •   R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data and Analytics)Dec 29, 2013 
  •   Learn R in a DayOct 30, 2013 
  •   Process Mining: Data Science in ActionApr 16, 2016  
  •   Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence AlgorithmsDec 25, 2016
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