Finished Machine Learning Course - What's next?

After 2 months, I finally finished the course. Yay!

Thank you professor Andrew Ng. for this excellent course. I learned a lot. I'll go over the materials again and again because these are all fundamental concepts, I must master them.

Many of the concepts/proofs were skipped because they're not the main focus for this course, but I feel I need to know and understand them in order for me to retain all the information.

Neural Network was barely touched. It was the hardest programming exercise of the whole course for a reason.

All my notes and codes were posted here: https://github.com/hhoangnguyen/MachineLearning

Advise for those who want to take the course:

• Understand basic Calculus, Differential derivative, Probability,  Statistics concepts
• Linear algebra. You must know this. It will make programming exercises much much easier
• Time, yes, it is actually time consuming. Each programming exercise took 4-6 hours for me
• Do your own research if you don't understand a concept, write a blog to document your journey
• Checkout FAQs, assignment hints, discussion forum, errata, resources of the course. They are extremely helpful
• Octave
• Do it. You lose nothing

What's next? Below are possible things that I plan (hope) to do:

• I'll start learning more and deep into some concepts (hints: Deep Learning)
• Taking Neural Networks course by professor Geoffrey Hinton
• Familiarize myself with Python, Notebook, TensorFlow
• Real world applications from Kaggle
• Read books: Learning From Data by Yaser Abu-Mostafa, Deep Learning by Ian Goodfellow

I plan to use this blog to post my work/learning about Machine Learning.

This is not the end. This is just the beginning. Challenge accepted! ;)