🇬🇧 If you want to learn Machine Learning, take this Scikit-learn course!
TLDR; 🙃
- Scikit-learn has its own course here: https://inria.github.io/scikit-learn-mooc/index.html
- It’s a free Machine Learning course
- It’s very educational
- It has a tons of examples to get familiar with scikit-learn
Teaching Machine Learning
Teaching AI and Machine Learning is far from easy. You can find so many resources on this subject: youtube videos, blog posts, MOOCs… As you can imagine, the quality is very heterogeneous.
I personally know this as I give AI courses for different companies. According to the background of your students, you need to adapt the way your approach such abstract concept as overfitting or cost function.
A few months ago, I saw that the team behind the amazing machine learning library Scikit-Learn released a full course about Machine Learning. I personally teach (and use) scikit-learn. This is the de-facto library to start with Machine Learning.
I bookmarked the link for later. And “later” was this week as I cleared my agenda for July.
Well, I sincerely invite you to take a look at this course if you want to dive in this subject. It is very well done. The approach is very educational: clear explanations, a tons of examples, not too many ideas at the same time, a lot of quizzes… It is really great ! In fact, I’m going to take some inspiration from it for my next courses.
Scikit-learn course
Alright, I’ll describe you some of the key elements of this course.
This course is divided into 10 modules:
- Machine Learning Concepts
- The predictive modeling pipeline
- Selecting the best model
- Hyperparameter tuning
- Linear models
- Decision tree models
- Ensemble of models
- Evaluating model performance
- Feature selection
- Interpretation
In every module, first you have a clear description about the key concepts. Then, you are guided through some examples, using scikit-learn. Then, you have an exercise you need to do. The solution is available (of course). And several quizzes punctuate the course.
If you need a complete introduction in Machine Learning you’ll learn everything you need thanks to this course :
- data exploration
- handle numerical and categorical data
- overfitting and underfitting
- hyperparameter tuning
- linear and tree-based models
- regularization
- ensemble methods
- cross-validation
- feature selection …
The only prerequisite is to know a bit about python. Then you’ll discover numpy, pandas and scikit-learn.
Conclusion
This Scikit-learn course is a must-do if you want to discover Machine Learning.
If you should take one course online, take this one! You won’t be disappointed.
This is very well written, crystal clear explanations, real examples, a lot of graphs.
If you have others resources to share, please, feel free to send them to me. I’ll be glad to hear about them.
Have a nice day
Maxime 🙃
