mmhansen / learn-machine-learning

learn-machine-learning

The plan

The plan is to get familiar with data science and its subset, machine learning.
This is a place to keep awesome resources as well as my written learning guide for the next year.

I will keep a record of every article/resource/video that I watch/read and the dates I do so.

For now, I'm just assembling tons of resources, but it will take a more certain shape once I understand the logical order of difficulty more clearly.

The curriculum

1. VIDEO - Intro to Python
2. VIDEO - ML with Python
3. COURSE - CS231 Stanford NOTES - CS231 Stanford
4. Stanford Deep Learning
5. BOOK - Advanced data analysis by Shalizi
6. BOOK - Mathematical applications of stats
7. COURSE - Intro to machine learning - Carnegie Melon
8. COURSE - Practical Data sciene - Carnegie Melon
9. BOOK - Python Data Sciene

The timeline

04-10-17 ARTICLE - Learn machine learning
04-10-17 BOOK - Think like a computer scientist
04-10-17 COURSE - Basic Linear Algebra

NEXT UP - Python the hard way

Extras

Machine learning guide
Tensorflow fizzbuzz

r programming language
python programming language

hard shit

1. Elements of Statistical Learning: http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf

2. Andrew Ng's Coursera Course: https://www.coursera.org/learn/machine-learning/home/info

3. The Deep Learning Book: https://www.deeplearningbook.org/front_matter.pdf

4. Put tensor flow or torch on a linux box and run examples: http://cs231n.github.io/aws-tutorial/

5. Keep up with the research: https://arxiv.org

more books

ML awesome
Data science awesome
List of beginner courses
Maths for ML
OSS

Numpy beginner video Pandas intro video

---

Start April 10th Finish August 18th 19 weeks for mathematical foundations.

1. Linear Algebra - Foundations to Frontiers
2. Applications of Linear Algebra Part 1
3. Applications of Linear Algebra Part 2
4. Calculus 1A: Differentiation
5. Calculus 1B: Integration
6. Calculus 1C: Coordinate Systems & Infinite Series
7. MIT OCW Multivariable Calculus
8. Introduction to Computer Science and Programming Using Python
9. Introduction to Computational Thinking and Data Science
10. Introduction to Python for Data Science
11. Programming with Python for Data Science
12. Introduction to Probability
13. Statistical Reasoning
14. Introduction to Statistics: Descriptive Statistics
15. Introduction to Statistics: Probability
16. Introduction to Statistics: Inference

20 weeks to end of year