the moment you feel you can make it in Machine Learning
Hey friend,
At first you feel overwhelmed when learning ML.
You have no clue which course to take. You have no clue if you will ever be able to complete a complex machine learning project.
But you persevere. You keep going without hoping things will become better.
As you go, you learn about various tools.
You learn about techniques how to achieve this or that.
How to split data into train and validation sets. How to work with large datasets. And many other small but essential techniques.
Also, you learn of good sources of information.
I haven't worked with transformers in a while. But if I had to, I know exactly where I would go to brush up on the ideas.
You also pick up on important perspectives. What is important on an ML project? How do people go about getting an ML job?
What has worked for others and what will likely work for you as well?
And at some point you hit the A-ha moment.
You realize that you will make it. That you have everything that you need.
That getting your first machine learning job, or losing that Junior from your title, is doable.
That there is no magic, that all there is to it are skill and execution. Two things ANYONE can pick up given enough time and good sources of information and inspiration.
The task stops being cryptic and mystical and becomes a road, possibly a long one, but one that you are perfectly equipped to cover. Just as has been the case for the hundreds of thousands of people that walked this path before you.
It is a beautiful feeling š. You may have experienced it already. But if you haven't, if you stay the course, you will.
My favorite things ā¤ļø
š How do you get to a point where you have the ML journey mapped out? - Of course, there is no definite answer that will withstand the test of time. You might have a good idea what to do focus on over the next couple of months, but beyond that horizon it's anyone's bet. So, in the short term, what to focus on if you are learning Machine Learning?
Despite the advance of generative models, I would still start with a solid foundation. The Fastbook by fast.ai is the best, most practical resource out there for learning the technical skills, and mindsets, necessary to practice Machine Learning at a high level. You can purchase a copy on Amazon but the version I linked to on GitHub is free!
Other than that I would listen to the stories of others, how they broke into the industry.
Unfortunately, for some reason, many people who pull off this transition don't want to talk about it!
Here are a couple of resources that come to mind that either directly or indirectly address this topic:
ā
šŗ How to Blog to Advance Your Career and Learn Fasterā
šŗ Jeremy Howard Interviews Kaggle Grandmaster Sanyam Bhutaniā
šŗ Fast.ai, Learning to Learn | Machine Learning, Kaggle & Blogging | Interview with Radek Osmulskiā
And I would certainly take a closer look at project based learning, which is what I describe in the the following blog post: Going From Not Being Able to Code to Deep Learning Hero.
Quote of the Week āļø
ā Ali Abdaal
The quote is a strong reminder to not overthink your plans and to start taking action as soon as possible. However, for best effects, this has to be combined with some idea of where you are headed, which is a result of research on how other have done it.
No one wants to fly entirely blind and that is probably a good thing, a reasonable approach.
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