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Michael Learns
Michael Learns

Posted on • Updated on

Finding the Right Course (Pt. 2)

Finding the Right Course

Welcome. Let’s begin, shall we?

The What

The first hurdle to my machine learning journey is, WTF is machine learning?

"Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.” - Source here

So basically it’s an application of AI that gives the system the ability to learn from experience without the “if/else” conditions.


Why would we want to learn machine learning? The simple answer would be why not? Having the power of creating something that could potentially make us as a human better that could help us in our daily lives is freaking awesome! Also, you could probably create your own JARVIS (like Tony Stark) which is kinda my goal.

Where To?

So now the question is how do I begin learning ML?

Well, first you’ll need a programming language to use. You can choose from Java, Python, C# and other languages. I’ve grown comfortable with Python and there are already lots of ML communities using python so for the rest of our journey, we’ll be using Python. I recommend the Codecademy course if you haven’t learned Python.

You’ve probably googled how to learn ML and you might have read somewhere that it takes a lot of knowledge of math to get started. That’s not really the case. Yes, learning the math can be very helpful, but sometimes, even the simplest knowledge can be very powerful.

So even with the simplest models and not too much math know-how, you can already do so so much! That’s great news ain't it?

Finding the Map...

Now to find a tutorial.
I’ve searched lots of ML tutorials around the web. I’ve checked out Sentdex, Udacity, Google, and Coursera. But I found that the best tutorial for me is Kaggle.

What is Kaggle? Taking from a notebook by Zeeshan-ul-hassan Usmani:

  1. "Kaggle is an AirBnB for Data Scientists.”
  2. "Kaggle enables data scientists and other developers to engage in running machine learning contests, write and share code, and to host datasets."

Check out Kaggle.

Kaggle hosts hundreds of thousands of datasets for data scientists and developers to use. It also hosts competitions that developers and data scientists can engage in. You could even win prize ($$$) on some of those competitions.

What many don’t know is that Kaggle also has a “Learn” section so that you too can start learning about ML, Deep Learning, Data Visualisation, and tons of other stuff!

Learning at Kaggle is so easy because it has these little “notebooks” which contain the text, the code, and the result from that code. These little notebooks easily explains concepts of the topics and what they do without driving you off to imagination land. Plus, the data used for the code is easily found within the "Data" tab of the notebook which you can download and experiment yourself. HURRAH!💥

Kaggle also has a great community mixed with experts and noobs like me. So you are never left out.

This post isn’t sponsored by Kaggle and I’m not in any way affiliated by it. I just really love learning at Kaggle ❤️

P.S. We all have different styles of learning. I love reading short articles that easily explains stuff in as little code as possible that can work for my particular use, and Kaggle easily does this for me. You should definitely checkout Sentdex, Udacity, Google, and Coursera as they offer great and comprehensive content that might also fit your learning style 😉

Top comments (4)

theaxmedia profile image
TheAxMedia • Edited

Hey Ben,
I'm just getting started reading your article/learning about ML and I like it so far, but please remove that quote from Albert E.! That is grossly taken out of context.
It means that only geniuses can (in physics) make theories shorter which is by Occam's Razor better/more desirable. Also, everybody can just bodge some subtheory onto existing ones. That's however really bad science.
If it was supposed to be a joke, I'm sorry. But then make it a bit more obvious, thanks.
Bye, AxM

imronlearning profile image
Michael Learns • Edited

Hahaha 😂 gotcha mate. Thanks for the clarification!

ewoks profile image

Hi Ben, thanks for the series on ML, it is really motivating to read about your adventure with it.
I wonder did you intentionally pointed to Python 2 course or you just missed newer one ?

Looking forward to more of posts about ML :)


imronlearning profile image
Michael Learns

Oh! I missed the newer course. Thanks for pointing that out. 😅