Guidance on next steps of Supervised Machine Learning

I recently completed the Course Supervised Machine Learning: Regression and Classification. I have this question from all experienced individuals, apart from starting the new course, what other things I should do after learning Supervised Machine Learning concepts? (In terms of practice, relevant experience, or projects)

I have heard of Kaggle, but I want wholesome advice on steps that anyone should take as a beginner with the goal of getting a job in this industry!!

PS. I liked the Supervised Learning course, But I am afraid I will forget the concepts if I start another course which is Advanced Learning Algorithms. So any advice around that?

I would say the best thing to do in regards to getting a job would be to think of your own project and tackle it. My reasoning:

  1. It is a heck of a lot harder to procure you own data from the untamed wilds of the internet
  2. There is a good chance nobody has used whatever data you scrape, so whatever analysis you do on it is guaranteed to be your original ideas and not some tutorial you copied
  3. It shows that you are motivated, dedicated and interested enough to do all that work on your own
  4. You can incorporate more aspects of programming into your final result eg. deployment/production

That said, I also think doing Kaggle’s are a great idea as well, particularly the competitions that have no monetary prize. Kaggle is a great platform for learning data science IMO because the community there is all about sharing information. Even in competitions with a prize of 100K the forums are full of people sharing advice, information and code. Most of the monetary ones are image/video based which sort of require knowledge of neural networks which it doesn’t sound like you have learned yet which is why I recommended the other ones.

Another thing you can work on are leetcodes. I haven’t experienced it, but apparently a lot of tech companies like to ask questions from here. It is also just a good idea to keep your skills sharp.

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