Data Science from Scratch: First Principles with Python
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
1121709189
Data Science from Scratch: First Principles with Python
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
65.99 In Stock
Data Science from Scratch: First Principles with Python

Data Science from Scratch: First Principles with Python

by Joel Grus
Data Science from Scratch: First Principles with Python

Data Science from Scratch: First Principles with Python

by Joel Grus

Paperback(2nd ed.)

$65.99 
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Overview

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

Product Details

ISBN-13: 9781492041139
Publisher: O'Reilly Media, Incorporated
Publication date: 06/11/2019
Edition description: 2nd ed.
Pages: 403
Product dimensions: 6.90(w) x 9.10(h) x 0.90(d)
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