Jupyter Notebook Version 추가

• "Data Science from Scratch" (2nd Ed.)의 내용을 Jupyter Notebook 버전으로 정리
• 관련 한글판 : "밑바닥부터 시작하는 데이터 과학"
  • 풍부한 예제 코드와 함께 머신러닝의 기본 모델들을 잘 설명한 훌륭한 교재
  • 머신러닝의 원리를 수학이 아닌 코드로 이해하고 직접 구현해 볼 수 있음
• 위치 : notebook
• 이해를 돕기 위하여 필요한 설명 및 예시, 코드를 일부 추가하였음
• 번역에 충실하기 보다 내용을 이해하고 전달하기 위하여 개념적인 설명은 원문과 다른 내용이 많음
• 책 내용중 일부 생략 (그냥 자습 가능한 내용, 개인적으로 관심없는 내용, 잘 모르는 내용^^)

기타 원본 소스는 저자의 Github 사이트와 동일함

(아래 내용 참고)

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 first-edition 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:

1. 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 the scratch directory itself, the imports won't work.

2. 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

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. [Deep Learning]
20. Clustering
21. Natural Language Processing
22. Network Analysis
23. Recommender Systems
24. Databases and SQL
25. MapReduce
26. Data Ethics
27. Go Forth And Do Data Science