I was interested in learning machine learning and deep learning ever since I realized its potential to apply to many research problems in power systems research. So I started learning it. So far, I have done one Coursera specialization, a course (you can probably guess this) and I'm currently doing another specialization. Before this, I went through several youtube videos to get a feel for what ML really is.
Apart from this, I'm enrolled in the program by Dr. Michal Fabinger @Tokyo Data Science
My goal here is to recommend the flow of this process, which would have been very helpful in the beginning for me. Although I have given Coursera links most of the content is available open-source for free. So here it goes,
- Search youtube for videos that explain what machine learning is. There are a lot out there. Don't get stuck in this just take one hour that should be enough. TensorFlow ML Zero to Hero
- Then start learning. A good place to start is Coursera - AMII specialization
- The next one I would recommend is Machine Learning - Andrew Ng which covers the theory behind machine learning. Also, this one is a must-do in the world of machine learning
- Then I would recommend Deep Learning - deeplearning.ai which covers deep learning (I did not do this and started doing the next one. I'll let you know how that goes)
- Then finally I would recommend Advanced Machine Learning - HSE
Also, there are several others I have planned to do in the coming months which are on ML tools.
- Getting Started with AWS Machine Learning
- Machine Learning with TensorFlow on Google Cloud Platform Specialization
- Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization
That's it for now. I'm sure there are others that are good resources but here I'm giving you my own path. Feel free to comment with your own suggestions.
