Data science from scratch: first principles with python 2nd edition, joel grus

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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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