So I started focusing on learning those first and foremost.
The absolute necessary ones were
- SciKit Learn
- Os (an important built-in package).
There were two really good Udemy courses that helped me here.
helped me learn the basics of the libraries mentioned above, in manipulating the data as well as visualizations.
helped me implement the algorithms using SkLearn and I also got my first introduction to Keras for Neural Networks here.
The practice of the taught concepts multiple times was necessary, I did that as much as I could and also read blogs on them
Everything that I did from there on was from Kaggle
. It is a platform that’s closest to exposing what real-life problems may look like.
A lot of people solve the same problem differently and you have to read a lot of other people’s code. From there, that’s what kept me growing.
Things I learned from Kaggle:
- Exploration and visualization of data
- How to approach a new problem
- Better code structure for implementing a machine learning solution
You don’t have to be an absolute grandmaster of kaggle but plenty of practice and patience is needed.
Then I just picked up some common projects and implemented them, mostly from kaggle. Like,
- Titanic Survival
- Spam Classification
- Movies Recommendation
- Boston House Pricing
- Churn Prediction
You’ll kind of know your way from there, moving to harder problems slowly.
Don’t overthink about maths more than it is necessary. You can always learn the mathematical concepts that you may be missing as you go.
Learning the maths behind will keep things interesting if you won’t enjoy that you will get bored pretty quickly.
And trust me on this, I flunked Maths in my junior college and had to redo the year but still, I kept my chin up.
Finally, it can get quite overwhelming at times, make sure to take one step at a time. Don’t focus on how far ahead other people are focus on how much you’ll grow from here.
And remember you are not too old or too young for this stuff. You can do this too!
All the resources I used don’t have to be the same, see what works for you better. Bend things your way.
Thanks for your time and for bearing with me, I hope it was worth it!
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Until next time!