Learnings
My approach towards getting good at ML — and Data Science in general — is inspired by this response by Andrew Ng on Quora. Here's the TLDR-poster version of it:
I try to follow this advice as well:
The AI autodidact degree.
— Emil Wallner (@EmilWallner) August 19, 2019
Pick three:
1. Top 20 in an ML competition
2. A published paper
3. An ML consulting gig/job
4. 50k AI readers, 30k video views, etc
5. An ML tool, 100 users/week
6. Master part one of Goodfellow’s DL book
7. A PR to TF, PyTorch, etc
This page is a work in progress; I'll eventually add descriptions to the courses.
Catalogue of MOOCs I've taken over the years; listed in reverse chronological order:
APIs and Microservices Certification
Offered By: 
Completed: Dec, 2019
Front End Libraries Certification
Offered By:
Completed: Nov, 2019
Responsive Web Design Certification
Offered By:
Completed: Nov, 2019
JavaScript Algorithms and Data Structures Certification
Offered By:
Completed: Nov, 2019
React.js Essential Training
Offered By: 
Completed: Nov, 2019
Data Engineering, Big Data, and Machine Learning on GCP Specialization
Offered By: 
Completed: Aug, 2019
Building Resilient Streaming Systems on Google Cloud Platform
Offered By:
Completed: Aug, 2019
Learning Docker
Offered By:
Completed: Aug, 2019
Intro to Self-Driving Cars Nanodegree
Offered By: 
Completed: Mar, 2019
I'm highly interested in the field of autonomous driving. This is the first course offered by Udacity in the field. It sets up the fundamentals — Bayesian thinking, Kalman filters, A* search, working with matrices in C++, etc. — needed for the much more comprehensive course 2 (which I'll be taking in the near future).
Deep Learning Specialization
Offered By: 
Completed: Nov, 2018
Sequence Models
Offered By:
Completed: Nov, 2018
Convolutional Neural Networks
Offered By:
Completed: Nov, 2018
Structuring Machine Learning Projects
Offered By:
Completed: Oct, 2018
Machine Learning for Coders
Offered By: fast.ai
Completed: Sept, 2018
This was the first fast.ai course that I took; solid practical introduction to ML.
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Offered By:
Completed: Sept, 2018
Neural Networks and Deep Learning
Offered By:
Completed: Sept, 2018
Java Build Automation With Maven
Offered By:
Completed: Aug, 2018
Machine Learning & AI Foundations: Decision Trees
Offered By:
Completed: Aug, 2018
Statistics Foundations 1
Offered By:
Completed: Aug, 2018
pandas Essential Training
Offered By:
Completed: Aug, 2018
Artificial Intelligence Foundations Neural Networks
Offered By:
Completed: July, 2018
Building and Deploying Deep Learning Applications with TensorFlow
Offered By:
Completed: July, 2018
Neural Networks And Convolutional Neural Networks Essential Training
Offered By:
Completed: July, 2018
Serverless Machine Learning with Tensorflow on Google Cloud Platform
Offered By:
Completed: Mar, 2018
Serverless Data Analysis with Google BigQuery and Cloud Dataflow
Offered By:
Completed: Mar, 2018
Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform
Offered By:
Completed: Mar, 2018
Google Cloud Platform Big Data and Machine Learning Fundamentals
Offered By:
Completed: Mar, 2018
Interactivity With Javascript
Offered By:
Completed: Jan, 2016
6.00.2x: Introduction to Computational Thinking and Data Science
Offered By:
Completed: Dec, 2015
Algorithms: Design and Analysis
Offered By:
Completed: Dec, 2015
Using Databases with Python
Offered By:
Completed: Dec, 2015
Databases: JSON Data
Offered By: 
Completed: Jan, 2015
Databases: SQL
Offered By:
Completed: Dec, 2015
Databases: XML Data
Offered By:
Completed: Nov, 2015
Relational Algebra
Offered By:
Completed: Oct, 2015
Internet History, Technology, and Security
Offered By:
Completed: Oct, 2015
Introduction to CSS3
Offered By:
Completed: Oct, 2015
Using Python to Access Web Data
Offered By:
Completed: Sept, 2015
Responsive Website Basics: Code with HTML, CSS, and JavaScript
Offered By:
Completed: Sept, 2015
Python Data Structures
Offered By:
Completed: Sept, 2015
This course comes from the great and powerful Professor Chuck Severance. He's one of the most relatable professors out there, and has single-handedly made learning Python easy — and fun — for thousands of people through his courses (they're available free of charge now!). Had the privilege of meeting him in October, 2018 at PyCon; super awesome guy.
Introduction to HTML5
Offered By:
Completed: Sept, 2015
6.00.1x: Introduction to Computer Science and Programming Using Python
Offered By: 
Completed: Sept, 2015
This was the first ever CS MOOC I took, which engendered in me, an intense curiosity for the field. This is a solid introduction to Python, as well as the fundamentals of CS. Professor Eric Grimson is one of the finest teachers out there, period.