joelgrus/datasciencefromscratch
code for Data Science From Scratch book
repo name  joelgrus/datasciencefromscratch 
repo link  https://github.com/joelgrus/datasciencefromscratch 
homepage  
language  Python 
size (curr.)  767 kB 
stars (curr.)  4709 
created  20141109 
license  MIT License 
Data Science from Scratch
Here’s all the code and examples from the second edition of my book Data Science from Scratch. They require at least Python 3.6.
(If you’re looking for the code and examples from the first edition, that’s in the firstedition
folder.)
If you want to use the code, you should be able to clone the repo and just do things like
In [1]: from scratch.linear_algebra import dot
In [2]: dot([1, 2, 3], [4, 5, 6])
Out[2]: 32
and so on and so forth.
Two notes:

In order to use the library like this, you need to be in the root directory (that is, the directory that contains the
scratch
folder). If you are in thescratch
directory itself, the imports won’t work. 
It’s possible that it will just work. It’s also possible that you may need to add the root directory to your
PYTHONPATH
, if you are on Linux or OSX this is as simple as
export PYTHONPATH=/path/to/where/you/cloned/this/repo
(substituting in the real path, of course).
If you are on Windows, it’s potentially more complicated.
Table of Contents
 Introduction
 A Crash Course in Python
 Visualizing Data
 Linear Algebra
 Statistics
 Probability
 Hypothesis and Inference
 Gradient Descent
 Getting Data
 Working With Data
 Machine Learning
 kNearest Neighbors
 Naive Bayes
 Simple Linear Regression
 Multiple Regression
 Logistic Regression
 Decision Trees
 Neural Networks
 [Deep Learning]
 Clustering
 Natural Language Processing
 Network Analysis
 Recommender Systems
 Databases and SQL
 MapReduce
 Data Ethics
 Go Forth And Do Data Science