I have come across a few great resources that I wanted to share. For students taking a machine learning class (like Northwestern University’s MSDS 422 Practical Machine Learning) these are great references, and a way to learn about them before, during, or after the class. This is not a comprehensive list, just a starter.

**Textbook**

There is a free online textbook, Neural Networks and Deep Learning.

**Videos**

There is a great math visualization site called 3Blue1Brown and they have a YouTube channel. There are 4 videos on neural networks/deep learning which are really informative and a good introduction.

- But what *is* a Neural Network? Chapter 1, deep learning
- Gradient Descent, how neural networks learn. Chapter 2, deep learning
- What is backpropagation really doing? Chapter 3, deep learning
- Backpropagation calculus. Appendix to deep learning chapter 3.

There is a great playlist on Essence of linear algebra, which is a great review and explanation of linear algebra and matrix operations. I wish I would have seen this when I was learning it.

**Scikit-Learn Tutorials**

There are tutorials on the Scikit-Learn site.

**TensorFlow tutorials**

They provide a link to this Google “Machine Learning Crash Course” – Google’s fast-paced, practical introduction to machine learning.

The TensorFlow site has a Tutorials page. There are tutorials for Images, Sequences, Data Representation, and a few other things.

**Google AI**

Google has it’s own education site (which also has the Machine Learning Crash Course referenced above).

**Blog sites**

**Adventures in Machine Learning**, Andy Thomas’s blog.

This is a must view site, and worth visiting several times over. Andy does a great job explaining the topics and has some great visuals as well. These are fantastic tutorials. I have listed only a few below.

Neural Networks Tutorial – A Pathway to Deep Learning

Python TensorFlow Tutorial – Build a Neural Network

Convolutional Neural Networks Tutorial in TensorFlow

Word2Vec work embedding tutorial in Python and TensorFlow

Recurrent neural networks and LSTM tutorial in Python and TensorFlow

**colah’s blog** – Christopher Olah’s blog

Another great blog, with lots of good postings. A few are listed below.

Deep Learning, NLP, and Representations

Neural Networks, Types and Functional Programming

**Courses**

**DataCamp** – one of my favorite learning sites. It does require a subscription.

DataCamp currently has 9 Python machine learning courses, which are listed below. They also have 9 R machine learning courses.

Machine Learning with the Experts: School Budgets

Deep Learning in Python

Building Chatbots in Python

Natural Language Processing Fundamentals in Python

Unsupervised Learning in Python

Linear Classifiers in Python

Extreme Gradient Boosting wiht XGBoost

HR Analytics in Python: Predicting Employee Churn

Supervised Learning with Scikit-Learn

**Udemy courses**

Udemy is also a favorite learning site. You can generally get the course for about $10.

My favorite Udemy learning series is from Lazy Programmers Inc. They have a variety of courses. Their blog site explains what order to take the courses in. There are many other courses from different instructors as well.

Deep Learning Prerequisites: The Numpy stack in Python

Deep Learning Prerequisites: Linear Regression in Python

Deep Learning Prerequisites: Logistic Regression in Python

Data Science: Deep Learning in Python

Modern Deep Learning in Python

Convolutional Neural Networks in Python

Recurrent Neural Networks in Python

Deep Learning with Natural Language Processing in Python

Advanced AI: Deep Reinforcement Learning in Python

Plus many other courses on Supervised and Unsupervised Learning, Bayesian ML, Ensemble ML, Cluster Analysis, and a few others.

If you have other favorite machine learning resources, please let me know.