Tips For Learning ML If You Don't Like learning Via Courses
I read a Reddit post about how the OP was struggling to learn ML. Because he found the courses abstract and theoretical. And did not see how it would relate to his ML goals. In the people gave their opinions and useful suggestions.
Some of those suggestions I will be showing below:
Create A Side Project
Start working on a project that would involve ML. Then you can learn about the topic as you’re developing the project. You can write about what learned in a blog post, so you know what to work on next time.
Implement A Paper
Implementing a paper helps learn new concepts and forces you to translate that knowledge into a tangible item.
Take Courses That Focus On Coding Models Straight Away
I recommend FastAI which is a very hands-on course. Which focuses on working on ML examples straight away. This course helps you learn the basics of Deep Learning while of some tangible examples to show.
Tutorials Provided By PyTorch and Tensorflow
You can try the tutorials provided on their websites. You will work through practical examples on how to use the library. And you can read about the concepts that some tutorials talk about along the way.
Create Your Favourite Models From Scratch
This idea is from Andrew Trask. You create neural networks only using NumPy. This will force you to turn any theoretical knowledge you have about ML into real-life examples. It won’t be enough to name a concept and move on. But you will need to make tangible examples of the concepts. This can be done for your favourite libraries as well.
Additional note:
You still need theoretical knowledge if you want to do well with ML. As want to know how your model works behind the scenes. And it helps you grasp any new concept that comes your way. If you want to learn about maths. Check out these resources (MML book and YC Learning Math for Machine Learning). As maths is something that many people struggle with when learning ML.
After this, you should be more confident about learning ML. And have hands-on experience making models and a greater understanding of courses you were watching.
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