Many machine learning courses that target developers want you to start with algebra, calculus, probabilities, ML theory, and only then—if you haven't quit already—you may see some code.

I want you to know there's another way.

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2. For me, there's no substitute to seeing things working, trying them out myself, hitting a wall, fixing them, seeing the results.

A hands-on approach engages me in a way pages of theory never will.

And I know many of you reading this are wired just like me.

3. I feel that driving a car is a good analogy.

While understanding some basics are necessary to start driving, you don't need to read the entire manual before jumping behind the wheel.

As long as you practice in empty parking lots and backroads, you'll be fine.

4. As you make your way to public roads, you can start incorporating more of the theory that will help you stay safe.

At this point, that theory won't be lost on you: your hours behind the wheel will help you make the necessary connections.

Things will start clicking quick.

5. I've talked to people struggling with derivatives that have no idea why or when they'll become helpful.

I've seen others memorizing what eigenvectors are, or manually transposing matrices because "that's what it takes."

Honestly, for the most part, it's not.