This is my first blog so I guess it’s gonna be very Naive. I hope it’s gonna be like Reinforcement Learning in which my skills will improve with the rewards associated with writing blog posts 🙂
In this article, I am going to design a roadmap for the 1 year younger myself who knew nothing about ML or Data Science. He was simply looking for a thing which he would be interested in. If this roadmap was available to me at that time, I hope it would have made my past 1-year learning journey somewhat smooth and efficient.
I am going to talk about courses that one should start with and in what order.
It’s an ML course offered by Stanford University on Coursera by Andrew Ng. It’s a wonderful course, and after 2–4 weeks, you will understand whether you are having an interest in ML or not. After this course only, I understood that I like this field.
One Problem with this course is that the programming assignments are in Octaves. So, it’s difficult for anyone to learn octave for this. The solution for this problem is that you can google search for Unsolved Assignments for this course in Jupyter Notebook files.
This course is offered by Consulting and Analytics Club, IIT Guwahati every year in Summers. You can check for this on their Facebook Page. They have created a very nice Learning Resources website which contains many articles and webpages to go through in order.
This website can give you Intermediate knowledge about Data Science. It also contains Data Scraping by Python, Resources to learn Python, Pandas, Numpy, and much more.
This website is absolutely free to access.
Its specialization in Deep Learning offered by deeplearning.ai and is taught by Andrew Ng on Coursera. It contains 5 superb courses which cover Basic Neural Nets, CNNs, NLP, etc. It also contains many cool guided projects.
It’s a very nice course on Coursera and it covers all key concepts in Keras Framework. In DL specialization, most of the assignments are in Keras but it does provide you enough knowledge about Keras. This course is short and very effective.
It is always advised to use the PyTorch framework instead of Tensorflow or Keras because the PyTorch community is very big now.
At the initial level of learning, you will find that it’s very complicated but after some time you will realize that it’s very convenient to use and you can get your code doubts cleared very easily as the community is very big.
I suggest Pytorch Tutorial by Python Engineer as it covers all the key things that you should know. If you did the above Keras course then this one will be very smooth for you like the order of things are similar.
The below-mentioned link is the Github repo for the code used in each video of the whole playlist. The link to the playlist is in the readme section. But I suggest that going through the ipymb code files will be sufficient. If you are having doubts about the code then you can go to a particular video and resolve it. The videos are very long and it would be very time-consuming to go through each and every video.
fast.ai is a high-level API built on PyTorch. Fast.ai also provides some free courses that are very helpful.
There are two parts in the DL course by fast.ai. The first part covers most of the implementation part and 2nd course covers more of the theoretical domain. I found this very helpful as it is in PyTorch.
They use the fast.ai library in codes but it’s up to you that you want to learn this library. But it’s okay to not use this and use only PyTorch code.
There is also fastbook repo which contains two types of code for each lesson. One type is the notebook used by Jeremy in the lessons with all the theory material and the other type have notebooks with only code cell and theory scraped. You can use the 2nd type to do experiments and all.
Use Google Colab instead of Jupyter Notebook as running on your laptop will consume a lot of Data as well as it would be not that computationally good as the Colab GPUs.
OpenCV python tutorial on youtube is a very good playlist. There are no prerequisites if you know basic python. You can go through this simultaneously with the above courses.
You can work on some Projects and Competitions on Kaggle simultaneously. One more important thing is that you should start blogging sooner.
Why you (yes, you) should blog
It’s a very good article by Rachael Thomas on why you should blog.
I hope this article will help in your journey.
I guess that’s it for my first blog. See you in the next one 🙂