Data Science from Scratch: First Principles with Python
Language: English
Published by O'Reilly Media, 2019
- Softcover
- Used
Seller: Blue Vase Books, Interlochen, MI, U.S.A.Blue Vase Books
AbeBooks seller since May 22, 2017
Condition: Used - Fair
US$ 23.51
Quantity: 1 available
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The item is very worn but is perfectly usable. Signs of wear can include aesthetic issues such as scratches, dents, worn and creased covers, folded page corners and minor liquid stains. All pages and the cover are intact, but the dust cover may be missing. Pages may include moderate to heavy amount of notes and highlighting, but the text is not obscured or unreadable. Page edges may have foxing age related spots and browning . May NOT include discs, access code or other supplemental materials.
Seller Inventory # BVV.1492041130.A
- Title
- Data Science from Scratch: First Principles with Python
- Author
- Grus, Joel
- Publisher
- O'Reilly Media
- Publication year
- 2019
- Condition
- acceptable
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1492041130
- ISBN 13
- 9781492041139
- Edition
- 2nd Edition
To really learn data science, you should not only master the tools―data science libraries, frameworks, modules, and toolkits―but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.
- Get a crash course in Python
- Learn the basics of linear algebra, statistics, and probability―and how and when they’re used in data science
- Collect, explore, clean, munge, and manipulate data
- Dive into the fundamentals of machine learning
- Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering
- Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
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Blue Vase Books
Interlochen, MI, U.S.A.
AbeBooks seller since May 22, 2017
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