Some resources on “How do I learn about machine learning?”
“How do I learn machine learning?” a question you’ve seen many times. Maybe you’re the one asking this question. In this blog post, I will provide some links that you can view.
While I’m not an expert. (And nowhere close to one). I can still point to some areas that you should check out.
Your first step is to enrol in the fast.ai course. This class will allow you try out ML firsthand. Without getting bogged down in lectures and theory at first. (Which is still important).
If you feel your python skills are not up to scratch. Then I recommend these resources:
Also, they are lots of YouTube videos explaining Python.
You should have a decent grasp of the basics after using these materials.
The second is some type of theory and maths:
There are various arguments about learning for ML. One argument says you should do it on a casebycase basis. For example, if you’re learning about CNNs then learning Linear algebra at the same time would be useful. The other case is to start learning the basic maths as you start. Maybe by following a course or reading a textbook.
In my opinion, I will argue for the first case. Because the ML field is so vast. That means you may get stuck learning about theory for a long time. Without getting much handson work. But if the second option sounds appealing then go ahead.
Resources for maths include:
Mathematics for Machine Learning
Learning Math for Machine Learning
For ML Theory:
Andrew Ng’s deep learning course
Yuan Lecun & co Deep learning course
Lectures via certain area:
Introduction to Convolutional Neural Networks for Visual Recognition (CS231n)
They are some textbooks that you try out:
Handson Machine Learning with ScikitLearn, Keras, and TensorFlow
Deep Learning with Python by Francois Chollet
The HundredPage Machine Learning Book
(I have not used these book myself, but gotten good reviews from various people.)
“But I don’t like learning via courses!” I wrote a blog post if that’s your case.
Related to that, you can continue to improve your skills doing more custom tasks, like:
Side projects
Kaggle
Implementing papers
Now after going through some of the material above. You know a bit more about Machine learning.
Now you can decide what to do next with that knowledge.

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