Data Science from Scratch First Principles with Python 2nd Edition by Grus Joel ISBN 1492041130 978-1492041139

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Product details:

ISBN 10: 1492041130 
ISBN 13: 978-1492041139
Author: Joel Grus

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

Table of contents:

Preface

1. Introduction

2. A Crash Course in Python

3. Visualizing Data

4. Linear Algebra

5. Statistics

6. Probability

7. Hypothesis and Inference

8. Gradient Descent

9. Getting Data

10. Working with Data

11. Machine Learning

12. k-Nearest Neighbors

13. Naive Bayes

14. Simple Linear Regression

15. Multiple Regression

16. Logistic Regression

17. Decision Trees

18. Neural Networks

19. Clustering

20. Natural Language Processing

21. Network Analysis

22. Recommender Systems

23. Databases and SQL

24. MapReduce

25. Go Forth and Do Data Science

Index

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