Now let us talk about what you can do before participating in a contest.
First of all, you need to choose a language: the competitions are usually hosted either in R or Python, sometimes in other languages like Julia, but mostly Python.
Once you have chosen the language, you can start practicing on real datasets. I recommend the UCI Machine Learning repository. Try to solve a simple problem like classification or clustering and see what happens. The datasets on UCI are grouped by the problem so it’s quite easy to orient yourself. Don’t forget to split the data set into a training set and test set and then also split the test set into a ‘public’ and ‘private’ set because that is how competitions on Kaggle are checked. For more information about cool ML datasets, you can also explore our blog.
In the Notebooks section on Kaggle or on GitHub you will probably find a solution to any simple problem that you’re trying to solve, maybe even using the same dataset that you use. Hundreds of people before you were also trying to learn ML. Feel free to use their notebooks for inspiration so you can get good at interpreting the results.
Practice on simple Kaggle contests that we have mentioned. Don’t start with the ones that offer hundreds of dollars as a prize. Use the notebooks and tutorials published by other participants to gradually grow your skills while also familiarizing yourself with the particularities of the platform. After your submissions start being graded high (top 10%-25%) in the leaderboard, you can start thinking of Featured or Research competitions.