---
title: Machine Learning - Resources
tags: CoderSchool
---


# Machine Learning Resources
The following is a list of free or paid online courses, blog posts, on machine learning, statistics, data-mining, etc.

## Courses

### [Machine Learning (Stanford University, Andrew Ng)](https://www.coursera.org/learn/machine-learning)
- Andrew Ng's course. Recommended for anyone who wants to start learning Machine Learning.
- Math approach, easy to understand.
- Covers basic Machine Learning algorithms.
- Theories + Practice in Matlab.

### [Neural Networks for Machine Learning (University of Toronto)](https://www.youtube.com/watch?v=cbeTc-Urqak&list=PLYvFQm7QY5Fy28dST8-qqzJjXr83NKWAr)
- Very basic concept and detailed of NN in Machine Learning.
- Explained by intuition, not much of Math.
- Theories 100%.

### [Deep Learning Specialization (by Andrew Ng, deeplearning.ai)](https://www.coursera.org/specializations/deep-learning)
- Recommended for anyone wanting to start with Deep Learning.
- Because it is Andrew Ng's.

### [Intro to Deep Learning (MIT)](http://introtodeeplearning.com/)
- Comprehensive but not too much in details.
- Including basic concepts and **big projects**
- Theories + Projects

### [Stanford's CS20 Tensorflow for Deep Learning Research](http://web.stanford.edu/class/cs20si/)
- Comprehensive of learning Tensorflow for Deep Learning
- Instructor: Huyen Chip
- Theories (Slides) + Assignments

### [fast.ai](https://www.fast.ai/) - deep learning MOOC
- Including practical and also theoretical materials.
- Top-down approach

### [Machine Learning Specialization (University of Washington)](https://www.coursera.org/specializations/machine-learning)
- But the length of the course is quite long
- Contains Supervised (Classification, Regression) and Unsupervised learning (Clustering and Retrieval)
- Theories and Case studies

### [Machine Learning Course - Oxford](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
- Fundamental components of Deep Learning + CNN + RNN + Reinforcement Learning
- Lecture slides and video recordings.
- Practical part using Torch.
- Quite heavy on Math

### [Stanford's CS231n: CNNs for Visual Recognition](https://www.youtube.com/watch?v=vT1JzLTH4G4&index=1&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv) - Spring 2017 iteration, [Course website](http://cs231n.github.io/) has supporting material.
- Very detailed about CNN for Computer vision
- Including videos, assignments, and very good materials on Course website.
- Requires strong background of Math.

### [Machine Learning Crash Course (Google) - with Tensorflow](https://developers.google.com/machine-learning/crash-course/) - free
- Introductory (quite basic) level, appropriate for beginners
- Prerequisites: Mastery of Linear Algebra, Proficiency in basic programming
- Including videos, reading materials
- **Plus: Exercise to check understanding, notebook to practice programming Tensorflow, Playground to interact with NN**.
- **Plus: Include interactive examples within lectures.**

### [Machine Learning Mini Bootcamp Course (LambdaSchool)](https://lambdaschool.com/courses/data-science/intro/)
- Include lectures as jupyter notebook
- Include Coding and Sprint challenges.
- Generic theories and practices.
- Ranging from Math to ML models to NN to Reinforcements.

### [Microsoft Professional Program for Artificial Intelligence](https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/)
- Very comprehensive course with essential concepts inlcuding Math and Research skills
- Using NLTK - MS's Deep Learning framework.
- Including Homework, Lecture videos, and Reading materials

### [Open Machine Learning Course](https://github.com/Yorko/mlcourse.ai) with [articles](https://medium.com/open-machine-learning-course) on Medium
- Comprehensive Notebooks and blog posts on Medium
- Can be used for References / Additional Reading Material

### [Machine Learning A-Z (Udemy)](https://www.udemy.com/machinelearning/) - Hands-On Python & R In Data Science
- Popular course on Udemy.
- Introductory level for ML concepts, **very intuitive explanation.**
- Including Lab to practice.

## Books

### [Introduction to Applied Linear Algebra](http://vmls-book.stanford.edu/vmls.pdf)
- A good introduction to Applied linear Algebra from Standford. This can be good start if you want a solid knowledge of linear algebra before learning ML.

### [Introduction to Probability](https://drive.google.com/file/d/1VmkAAGOYCTORq1wxSQqy255qLJjTNvBI/view)
- A great introduction to Probability concept, which is essential for any Machine learning or data related techniques.

### [ESL](https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf) and [ISL](http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf)
- Beginner (ISL) and Advanced (ESL) presentation to classic machine learning from world-class stats professors.

### [Foundation of Data Science](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/11/book-June-14-2017pdf.pdf)
- Very comprehensive overview of Data Science essentials.

### [Deep Learning by Ian Goodfellow](https://www.deeplearningbook.org/)
- An advanced but very essential if you want to have a low-level understanding of Deep Learning concept.

## Blog Posts/Documentations/Tutorials
### [Pytorch Documentation](https://pytorch.org/tutorials/)
- Very comprehensive documentation of Pytorch.

### [Pytorch Tutorials](https://pytorch.org/docs/stable/index.html)
- Examples and tutorials from Pytorch team.

### [Computational Linear Algebra for Coders](https://github.com/fastai/numerical-linear-algebra/blob/master/README.md)
- This course is focused on the question: How do we do matrix computations with acceptable speed and acceptable accuracy?

### [Linear Algrebra for Reference by Standford](https://sgfin.github.io/files/notes/CS229_Linear_Algebra.pdf)
- Standford team's great note of Linear Algebra in CS229 course. If you want a quick overview but still comprehensive enough, this is the one.

### [Matrix Calculus for Deep Learning](https://explained.ai/matrix-calculus/index.html)
- Matrix calculus is a must for every Deep Learning practitioner. This is for you if you want to get a fast start with Matrix Calculus.

### [Lecture notes from Andrew Ng's intro to ML](https://sgfin.github.io/files/notes/CS229_Lecture_Notes.pdf)
- If you want a good mathematical perspective of Machine learning concepts, this lecture note of course CS229 is the one.

### [Natural Lanaguage Processing with DL notes - CS224](https://sgfin.github.io/files/notes/cs224n-2017-merged.pdf)
- Fantastic course notes on Deep Learning for NLP from Stanford

### [Neubig's Neural Network for NLP](http://www.phontron.com/class/nn4nlp2018/schedule.html)
- Great course from CMU's Graham Neubig. With lecture videos [here](https://www.youtube.com/playlist?list=PLbdKUKMAnh9Qqs5uwEBDfRb_L3YaLbRKq)

## Cheatsheet
### [Computer Science Theory Cheatsheet](https://www.tug.org/texshowcase/cheat.pdf)
- Comprehensive summary of necessary mathematics and computer science theories, ranging from graph theory to calculus, probability, ...

### [SQL Joins Cheatsheet](https://sgfin.github.io/files/cheatsheets/SQL_joins.png)
- Graphical description of classifc SQL joins
### [Python Pandas Cheatsheet](https://sgfin.github.io/files/cheatsheets/Python_cheatsheet_pandas.pdf)
- Cheatsheet for Python's data wrangling and manipulating, Pandas.
### [Python Numpy Cheatsheet](https://sgfin.github.io/files/cheatsheets/Python_cheatsheet_numpy.pdf)
- Cheatsheet for Python's efficient numerical package, Numpy.

### [Python Keras Cheatsheet](https://sgfin.github.io/files/cheatsheets/Python_Keras_Cheat_Sheet.pdf)
- Cheatsheet for Python's high-level API of Deep learning, Keras.

### [Python Sklearn Cheatsheet](https://sgfin.github.io/files/cheatsheets/Python_cheatsheet_scikit.pdf)
- Cheatsheet for Python's Machine learning package, sklearn.

### [Python Seaborn Cheatsheet](https://seaborn.pydata.org/tutorial.html)
- Tutorials and examples for Python visualization library, Seaborn.

### [Python Pytorch Cheatsheet](https://pytorch.org/tutorials/beginner/ptcheat.html)
- World-class quality cheatsheet from Pytorch, a deep learning library.

### [Probability Cheatsheet](https://sgfin.github.io/files/cheatsheets/probability_cheatsheet_blackwhite.pdf)
- Comprehensive overview of Probability concepts with illustrations.

### [Standford Machine Learning Cheatsheet](https://sgfin.github.io/files/cheatsheets/cs229_2018_cheatsheet.pdf)
- A great cheatsheer from Standford team with essential knowledge about Machine learning concept with examples and illustrations

## References
- [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning/blob/master/courses.md)
- [Machine Learning Resources](https://sgfin.github.io/learning-resources/?fbclid=IwAR0MwKLjjv_Dpgw8w7ofWbVfHZb5AV0LA-ZAU47k0iLuredsQMSzthkVBps)

## Miscellanious
- [KDE](https://mathisonian.github.io/kde/)
-