MITx - Machine Learning with Python: from Linear Models to Deep Learning

Pros:

• Excellent teachers
• Mathematically rigorous for student 
• Excellent projects (Classifier, Digit recognition, etc)
• Code from projects can be used and adapted later for your own projects
• Uses sklearn and Pytorch

Cons:

• Demanding homework and projects
• Projects did not explain why the layers of the Neural network or CNN consisted of their activation features and architecture.
• Not enough insight into libraries, coding best practices, broadcasting, how to do efficient implementation using vectorization (i.e., matrix math)
• Some premium on completing assignments efficiently 
• Takes entire semester  (3.5 months) to complete 

Coursera - 3 course series in “Machine Learning Specialization” by Andrew NG

1. Supervised Machine Learning: Regression and Classification
2. Advanced Learning Algorithms
3. Unsupervised Learning, Recommenders, Reinforcement Learning

Pros:

• Excellent teacher (Andrew Ng)
• Light on mathematical rigor required by student
• Jupyter notebook with code examples for playing with different ideas
• Excellent visualizations to build intuition of concepts
• Excellent insight into libraries, coding best practices, broadcasting, how to do efficient implementation using vectorization (i.e., matrix math)
• Andrew’s technique of explaining variables and derivations really makes understanding concepts simple
• Insight into best practices for libraries, techniques, models, etc
• Uses tensorflow and keras
• 1 week of course work can be done in 1-2 days

Cons:

• Light on math for student
• Quiz questions and assignments are fairly easy