bradleybossard/data-science-from-scratch repository overview

⁠Data Science from Scratch

Here's all the code and examples from my book Data Science from Scratch⁠. The code directory contains Python 2.7 versions, and the code-python3 direction contains the Python 3 equivalents. (I tested them in 3.5, but they should work in any 3.x.)

Each can be imported as a module, for example (after you cd into the /code directory):

from linear_algebra import distance, vector_mean
v = [1, 2, 3]
w = [4, 5, 6]
print distance(v, w)
print vector_mean([v, w])

Or can be run from the command line to get a demo of what it does (and to execute the examples from the book):

python recommender_systems.py

Additionally, I've collected all the links⁠ from the book.

⁠Table of Contents

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