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Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R, by Daniel D. Gutierrez
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A practitioner's tools have a direct impact on the success of his or her work. 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 machine learning and model evaluation.
Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set of essential tools we need for a long-term career in the data science field - recommendations are provided for further study in order to build advanced skills in tackling important data problem domains.
The R statistical environment was chosen for use in this book. R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work. All of the code examples for the book are written in R. In addition, many popular R packages and data sets will be used.
- Sales Rank: #1268518 in Books
- Published on: 2015-09-14
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .64" w x 7.50" l, .0 pounds
- Binding: Paperback
- 320 pages
From the Inside Flap
A practitioner's tools have a direct impact on the success of his or her work. 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 machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set of essential tools we need for a long-term career in the data science field recommendations are provided for further study in order to build advanced skills in tackling important data problem domains. The R statistical environment was chosen for use in this book. R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work. All of the code examples for the book are written in R. In addition, many popular R packages and data sets will be used.
About the Author
Daniel D. Gutierrez is a practicing data scientist through his Santa Monica, Calif. consulting firm AMULET Analytics. Daniel also serves as Managing Editor for insideBIGDATA.com where he keeps a pulse on this dynamic industry. He is also an educator and teaches classes in data science, machine learning and R for universities and large enterprises. Daniel holds a BS degree in mathematics and computer science from UCLA.
Most helpful customer reviews
0 of 0 people found the following review helpful.
The 20% that gets you 80% of your Results
By Jeff Weakley
I love this book. For what it is. And for what it is not. I’m new to data science and “what it is NOT” is very important to me. It’s NOT a lot mind-numbing math. It’s NOT long intellectual detours into academic thinking that doesn’t really apply to the kind of projects I’m wanting to do. What it is… is the Pareto Principle: the 20% of what you do that gives you 80% of your results. This is at least that 20-40%. It’s the real world basics of data science / machine learning that I can learn and use in the real world.
It’s also like having a mentor in a book form. Throughout this book, Gutierrez provide hard learned, hard working insights about how you actually do data science on a day-to-day basis. Little things that have a big impact and save lots of wasted time. Little things that you could spend the next 10 years learning yourself. Or you could read this book. I highly recommend it for people new or intermediate in their data science /ML journey.
0 of 0 people found the following review helpful.
Excellent and most importantly
By zhiyao pei
Excellent and most importantly, very updated book. This is the only data science book I read that heavily includes Gradient Boosting, which is the most popular method in Kaggle.
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