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[Doc] Data Science from Scratch: First Principles with Python
Data Science from Scratch: First Principles with Python details Details Product: 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 Author: Format: PDF / EPUB / MOBI E-Books are now available on this website Works on PC, iPad, Android, iOS, Tablet, MAC) Note: Before I apologize, here I am not offering it for free, but you have to join our service, and get a trial period of 14–30 days, you can cancel it if it is uncomfortable. Thank you so much .. Hope you are pleased to join our service, and you can read all the books you want ..
Data Science from Scratch: First Principles with Python details
Details Product: 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
Author:
Format: PDF / EPUB / MOBI
E-Books are now available on this website
Works on PC, iPad, Android, iOS, Tablet, MAC)
Note:
Before I apologize, here I am not offering it for free, but you have to join our service, and get a trial period of 14–30 days, you can cancel it if it is uncomfortable. Thank you so much .. Hope you are pleased to join our service, and you can read all the books you want ..
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[Doc] Data Science from Scratch: First Principles with Python
Description [Doc] 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,
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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