Geek Culture
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How to become very good at Machine Learning

How I taught myself complex ideas in Data Science, Machine Learning, and Computer Science

Key Highlights

  1. The current standard advice for beginners in ML- When it comes to telling beginners in ML how they should learn ML, the standard advice is to do a project. Often accompanied by a list of standard projects that every wannabe ML person has on their resume. Some even throw the ‘do a course’.
  2. Why this advice is bad- Projects and courses can be great because of the structure they provide. However, relying on them exclusively will turn this strength into a weakness. Projects will teach you how to do things. They will not teach you what to do. Many times, tutorials and courses do a lot of the legwork for you. Thus you will not be equipped for the messiness of real-world challenges.
  3. What you should do instead- Read. Yes, I say this a lot. It’s because this works. Specifically, start reading actual Machine Learning Research papers. Yes, even if you’re a beginner who knows very little. I’ll go over an overview of what to do in this article. A more in-depth piece on how to interact with very technical documents/talks will be covered on another Saturday.
  4. For best results- Once you come across ideas, now is when you can try mini-projects for learning how to actually implement these ideas. This will expose you to the coding and the various frameworks available to you. Best of both worlds.
Photo by Arseny Togulev on Unsplash

The Advice for Learning ML and why it’s incomplete

  1. Take a combination of Math Classes. Typically involving Probability and Statistics, Calculus, Linear Algebra, and Logic.
  2. Find one or two personal projects that interest you. Really good advice will suggest building an end-to-end pipeline starting from Data Collection all the way to analysis and report generation.
  3. Look at some tutorials/documentation online for learning the implementations.
  4. Enjoy your ML expertise.

One thing I noticed was that there was a big difference between the Machine Learning taught to Master’s Students and the Machine Learning research that was happening.

But I don’t have much expertise. Won’t papers be too complex?

What flaws?

Another example of me learning from experts. I recently covered a paper that showed Why Tree-Based Models Beat Deep Learning on Tabular Data and I didn’t understand one of the details. So I just asked and Jimmy was kind enough to give me an explanation. Exposing yourself to ideas will help you grow.

In a nutshell

How to Proceed

Photo by Stephen Dawson on Unsplash

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Devansh- Machine Learning Made Simple

Deep Insights about Artificial Intelligence (AI), Machine Learning, Software Engineering, and the Tech Industry. Follow me to come out on top