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Data Science from Scratch in pdf

 

Data Science from Scratch: First Principles with Python 2nd Edition by Joel Grus is PDF book for free download.

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. 

With this updated second edition, you'll learn how many of the most fundamental data science 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 hacking skills you need to get started as a data scientist. Today's messy glut of data holds answers to questions no one's even thought to ask. This book provides you with the know-how to dig those answers out.

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.

About the Author:

Joel Grus is a research engineer at the Allen Institute for Artificial Intelligence. Previously he worked as a software engineer at Google and a data scientist at several startups. He lives in Seattle, where he regularly attends data science happy hours.

CONTENTS:

Chapter 1. Introduction

Chapter 2. A Crash Course in Python

Chapter 3. Visualizing Data

Chapter 4. Linear Algebra

Chapter 5. Statistics

Chapter 6. Probability

Chapter 7. Hypothesis and Inference

Chapter 8. Gradient Descent

Chapter 9. Getting Data

Chapter 10. Working with Data

Chapter 11. Machine Learning

Chapter 12. k-Nearest Neighbors

Chapter 13. Naive Bayes

Chapter 14. Simple Linear Regression

Chapter 15. Multiple Regression

Chapter 16. Logistic Regression

Chapter 17. Decision Trees

Chapter 18. Neural Networks

Chapter 19. Deep Learning

Chapter 20. Clustering

Chapter 21. Natural Language Processing

Chapter 22. Network Analysis

Chapter 23. Recommender Systems

Chapter 24. Databases and SQL

Chapter 25. MapReduce

Chapter 26. Data Ethics

Chapter 27. Go Forth and Do Data Science

About the book:

Publisher ‏ : ‎ O'Reilly Media; 2nd edition (May 16, 2019)

Language ‏ : ‎ English

Paperback ‏ : ‎ 513 pages

Item Weight ‏ : ‎ 1.4 pounds

Dimensions ‏ : ‎ 6.9 x 0.9 x 9.1 inches

File: PDF, 10MB

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