Best FREE Machine Learning Crash Courses

Key Takeaways

hi everyone i'm patrick and in this video i present you three of my favorite free machine learning crash courses the emphasis is on crash course so it shouldn't take you too long to complete them in fact the first one takes only one hour the second one takes about 15 hours and the last one about seven hours as prerequisite the only thing you need is basic python skills so you don't need to be an expert but you should be able to code stuff and be familiar with basic data types and control flows functions loops if statements and stuff like that and it will be a little bit easier if you already have experience with numpy and pandas but you can pick up these skills on the go all right now let's have a look at the courses so the first one is called introduction to machine learning problem framing and this one is by google and by the way all the links are below the video so this one is for you if you're a complete beginner it only takes you one hour and it walks you through common ml terms then you learn how you can identify whether to solve problems with machine learning and then you also learn how you define a problem as a machine learning problem and how you can propose a solution so yeah this one gives you a pretty nice introduction so if you're a complete beginner then i recommend to take this one first the second course is also by google and is called the machine learning crash course so this one takes about 15 hours and along the way you will also learn how to use the tensorflow api so this is pretty cool so if you're not sure if you should take this one then you can click on view prerequisites and answer these questions is the machine learning course right for you and as a pre-work they recommend this introduction to machine learning problem framing course that i just told you and here you also have the option to take refresher courses for numpy and panda so this can be really helpful so let's click on start crash course and have a quick look at the course outline so at the left side we see the outline so here we see we start with machine learning concepts then if we scroll down we have a short section about machine learning engineering practices and about machine learning systems in real world with a few short examples so yeah the main section is the machine learning concept section so here you see for each section you have the estimate of the time that it needs and it's basically a mixture of video lectures written lectures and exercises that you can do in a google collab so yeah this is pretty cool for example let's click on the classification lecture then we see we start with a video lecture and again here we can see the estimated time for this specific lecture then if we click on the next one then this is a written tutorial and at the end we have a programming exercise that we can start in a google collab and then we get the hands-on experience so this is always great so let's have a more detailed look at these machine learning concepts so we start with machine learning framing so again here we learn about machine learning terminology then descending into ml so here we learn about our first machine learning algorithm linear regression then how to reduce the loss then we have first steps with tensorflow then we learn about training tests and validation sets then about regularization logistic regression classification neural networks multi-class neural networks and embeddings so yeah i think this is a pretty good outline it gives you an introduction to most of the important concepts and as i said along the way you will learn how to use tensorflow and you get a hands-on experience and can do all the programming exercises and a google call up so yeah this course is the longest one with 15 hours but the course outline is pretty extensive and you will learn a ton of stuff so i recommend to do this if you're serious and want to get started with ml so the last course i recommend is on kaggle so if we go to kaggle.com learn then we actually find a lot of different courses and they are all free and we are able to earn certificates so the ones i recommend are actually two courses so it's called intuitive machine learning and intermediate machine learning so i recommend to combine them the first one takes about three hours and the second one about four hours so then seven in total so let's explore the site a little bit more then we see we also get a course on pandas for example for data visualization and we even get one intro to deep learning so yeah i think this is pretty cool so let's start the intro to machine learning and the intermediate machine learning and have a look at the outline so basically for all courses you have a written tutorial and then you also get a exercise that you can do and then you have immediately a hands-on experience we see here the overview three hours eight lessons and we learn how models work we learn about basic data exploration our first machine learning model model validation under fitting and overfitting then random forests and how we can take part in machine learning competitions and let's have a look at the intermediate course so here we learn about missing values categorical variables pipelines cross validation xg boost and data leakage so yeah again i think the outline is pretty nice for these two courses so let's have a look at for example the first real election with an exercise basic data exploration then we get a written tutorial so here we don't have video lectures we just have a written tutorial but they are also pretty nice and then at the end we can start the coding exercise and this will again open a notebook that we can then do and yeah i think this is pretty cool as well and yeah i recommend to check out kaggle.com learn and have a look at the available courses so yeah these are my recommendations i hope you enjoyed this list and if you know about any other machine learning crash courses then let me know in the comments below and then i hope to see you in the next video bye