Data Science from Scratch: First Principles with PythonData 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. |
Contents
Chapter 1 Introduction | 1 |
Chapter 2 A Crash Course in Python | 13 |
Chapter 3 Visualizing Data | 41 |
Chapter 4 Linear Algebra | 51 |
Chapter 5 Statistics | 59 |
Chapter 6 Probability | 71 |
Chapter 7 Hypothesis and Inference | 83 |
Chapter 8 Gradient Descent | 95 |
Chapter 16 Logistic Regression | 197 |
Chapter 17 Decision Trees | 209 |
Chapter 18 Neural Networks | 221 |
Chapter 19 Deep Learning | 233 |
Chapter 20 Clustering | 257 |
Chapter 21 Natural Language Processing | 273 |
Chapter 22 Network Analysis | 303 |
Chapter 23 Recommender Systems | 315 |
Chapter 9 Getting Data | 105 |
Chapter 10 Working with Data | 123 |
Chapter 11 Machine Learning | 147 |
Chapter 12 kNearest Neighbors | 159 |
Chapter 13 Naive Bayes | 169 |
Chapter 14 Simple Linear Regression | 179 |
Chapter 15 Multiple Regression | 185 |
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