For those of you that are nervous, and feel like you're not even ready for Kaggle, then hop on coursera. And start yourself off with this course. Its an amazing introductory course to all the essential aspects of machine learning. However, it can get complicated at times. So be ready to look up a lot of the information on your own. What helped me through it was articles on this website, I literally searched up whatever question I had, and they had some sort of answer. I’m giving you all a huge resource and you don’t even know it yet, that website (towardsdatascience) is a huge help. You’ll see why in the future.  

Okay, now that we have some prerequisites out the way, let me say a few more things. Machine learning is a huge field, consisting of tons of different ways to approach problems. And what no one tells you at first, and trust me, this is coming from the kid that picked this stuff up from reading arxiv articles form Cornell's database, is that the easiest place to start in this community is from the fundamental models. You can't just jump into Neural Networks; you won't truly understand what's going on. You need to start with linear regression, logistic regression, decision trees, random forest, and K-means. And then once you’ve learned these and picked up on the ways you can permutate these models you'll be much better off when going into stuff like reinforcement learning, general adversial networks, and Neural Networks.   

So, what I’m going to do is give you guys excellent places to start your very first machine learning models (straight from the fundamentals). Whilst supplying excellent places to really learn about them as well. Here it is: 

Check out my articles on medium as well. I go over how to address collinearity in this one. I talk about decision tree hyper-parameters in this one. And I do a guided project from coursera in this one. There all amazing articles that explain a lot!