ML Resources I use Everyday

Hello folks! Today I'll be writing down the ML Resources I use every day to travel along the journey from being clueless about anything to getting project ideas and finally implementation. Let's get right into it!

Ideation:

For any ML project, it is very important to have a clear idea and objectives in mind, but the main question is how we get an idea that involves defining a problem statement, basic solution architecture and its implementation plan.

• Reddit: Although Reddit is not a resource to learn hardcore ML and DL concepts, it is a great source of inspiration for different ideas and projects. Two good subreddits that I'd recommend are r/learnmachinelearning and r/Python.

• Blogs: I understand in the era of reels and shorts, blogs seem to be too old-school, but good blogs are quite detailed about the specific topics they cover and the greatest advantage of blogs is that reader can learn at their own pace. Some amazing resources that I tend to visit daily are as follows:

  • Towards Data Science (Medium articles specific for Data Science and Machine learning)

  • KDNuggets (These are more technical and longer than other blogs but can be used as a great source to learn new things)

  • Analytics Vidhya (This India-based blog covers variety of topics at different levels for different types of readers and with its daily challenges, this site offers a lot of content)

• Some other niche blogs I refer to when I am exploring some specific areas are:

  • PyImageSearch (I refer to this blog specifically for Computer Vision concepts, this can be a really good resource to learn a variety of CV-related things like OpenCV, Object detection, Key-point tracking, GANs etc.)

  • Machine Learning Mastery (This blog covers more generalized machine learning concepts, it offers some amazing insights you shouldn't miss)

Implementation:

• Papers With Code: After the ideation phase, we need to focus more on the implementation aspects of our ML work. And, likely, someone has already done something similar to our idea and it is better to use their existing work rather than reinvent the wheel. This resource provides research papers and their corresponding codebase for readers which are sorted based on subject area.

• GitHub: GitHub is one of the largest platforms that hosts the majority of the code repository of all either public or private. We can use GitHub repositories to search our application-specific architectures for setting up the base of our ML project. GitHub ranks the best repositories based on the number of stars, forks etc. which helps us to find required resources easily.

Theory:

While working on the ideation or implementation phase of our ML projects, it is crucial to have the fundamental knowledge of the underlying technology which gives us more clarity and helps us to explain to others with ease, debug our bugs more efficiently and improve the overall performance of models. I prefer books and YouTube channels for understanding the theoretical aspects:

Books:

1. Deep learning with Python by Francois Chollet

2. Data Science from Scratch by Joel Grus

3. Hands-on Machine Learning with Scikit-Learn, Keras and Tensorflow by Aurelien Geron

4. Approaching (Almost) Any Machine Learning Problem by Abhishek Thakur

5. Stanford CS229 (Machine Learning) Lecture Notes by Dr. Andrew Ng

YouTube:

1. CS229 Machine Learning by Stanford

2. Neural Networks: Zero to Hero by Andrej Karpathy