Step by step guide to become a Machine Learning Expert during Coronavirus Lockdown

This is a step by step guide to become a deep learning expert during the Coronavirus Lockdown. There is lot of doom and gloom outside and we are all stuck at home. I wish best of health to all my readers. On the positive side, we can use this time to gain expertise in any field of choice. The goal of this blog post is to show you how to gain in depth ML Expertise. I am also personally following this plan to brush myself up. My stretch goal is also to explore other areas, especially biotechnology and how AI could enable the development of the field for the next 10 years. However, details about that would be the content of another post. This post will primarily focus on Deep Learning and will cater to anyone who has some experience in programming and wants to get into the field.

Before we get into the learning plan, here is a motivation video of how the first 20 hours of learning goes

Step 0 : Learn the classical ML course by Andrew Ng

This is the recommended course for anyone who wants to learn ML. This doesnt need you to know calculus and Andrew Ng's style will make the learning fun and intuitive

• Youtube course link of the coursera course
• CS229 - stanford course notes

Step 1 : Learn the basics of deep learning from Andrew Ng

Take the Deeplearning.ai course. Here are the video lectures from youtube with course notes links. Classic Andrew Ng style, will help you build intuition and understanding of neural networks and various complex concepts.

1. Neural Networks and Deep Learning 👉April 7th
2. Improving deep neural networks : hyperparameter tuning, regularization and optimization 👉April 8th
• Notes for this course available here 
3. Structuring Machine Learning Projects
4. Convolution Neural Networks 👉April 9th
5. Sequence Models  👉April 10-13th

Step 2 : Learn PyTorch

I would recommend this after you have understood the Andrew Ng lectures. PyTorch was developed at Facebook and is a powerful tool for deep learning practitioners. 

• Introduction to deep learning with pytorch course taught by Facebook Artificial Intelligence team (April 21)
• Github repo of all the pytorch tutorials from the udacity course

Step 3 : Learn from Jeremy Howard of fast ai

This is a very practical course for all engineers who want to get into deep learning. 

• Part I - Practical deep learning for coders
• Part II - Deep Learning foundations

Step 4 : Participate and practice in Kaggle

If you have reached here, you have gained great knowledge already. Time to put things into practice and help the world during this coronavirus pandemic. Take part in these kaggle competitions and many more

• Covid global forecasting
• https://www.kaggle.com/uvinetz/week-2-submission#Modeling

Step 5 : Miscellaneous reading list to supplement/continue learning during/post lockdown

• CS231n lecture notes - Computer Vision course from Stanford
• Personalization and Recommender Systems Papers
• PyTorch Kaggle Kernels
• KDD 2019 videos 
• Capsule Networks
• Newsletters
• https://www.getrevue.co/profile/wildml 
• deeplearning.ai 
• Huggingface's weekly awesome papers
• Hugging face bert documentation
• Bert kaggle notebook
• NLP papers to read
• Exploring LSTMs
• The unreasonable effectiveness of RNNs