A few books from my bookshelf on data science & machine learning

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

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Storytelling with data

Storytelling with data

Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation.

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The Bestseller Code

The Bestseller Code

Ask most people about massive success in the world of fiction, and you’ll typically hear that it’s a game of hazy crystal balls. The sales figures of E. L. James or Dan Brown seem to be freakish―random occurrences in an unknowable market. But what if there were an algorithm that could reveal a secret DNA of bestsellers, regardless of their genre? What if it knew, just from analyzing the words alone, not just why genre writers like John Grisham and Danielle Steel belong on the lists, but also that authors such as Junot Diaz, Jodi Picoult, and Donna Tartt had telltale signs of success all over their pages?

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Predictive Analytics

Predictive Analytics

Author:

An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques.

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The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t

The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t

Author:

Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of FiveThirtyEight.com.

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Deep Learning

Deep Learning

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

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Machine Learning with R

Machine Learning with R

Author:

R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required - this book will take you methodically through every stage of applying machine learning.

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Python Machine Learning

Python Machine Learning

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics

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Data Wrangling with Python: Tips and Tools to Make Your Life Easier

Data Wrangling with Python: Tips and Tools to Make Your Life Easier

How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access. You don't need to know a thing about the Python programming language to get started.

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Data Science from Scratch: First Principles with Python

Data Science from Scratch: First Principles with Python

Author:

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.

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R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics

R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics

Author:

With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.

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Dune (The inspiration for the name of my company :))

Dune (The inspiration for the name of my company :))

Author:

Science fiction’s supreme masterpiece, Dune will be forever considered a triumph of the imagination. Set on the desert planet Arrakis, it is the story of the boy Paul Atreides, who will become the mysterious man known as Muad’Dib. Paul’s noble family is named stewards of Arrakis, whose sands are the only source of a powerful drug called “the spice.” After his family is brought down in a traitorous plot, Paul must go undercover to seek revenge, and to bring to fruition humankind’s most ancient and unattainable dream. A stunning blend of adventure and mysticism, environmentalism and politics, Dune won the first Nebula Award, shared the Hugo Award, and formed the basis of what is undoubtedly the grandest epic in science fiction.

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