Kindle Data Science from Scratch: First Principles with Python Full
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Book Description
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, Naive
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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