Course Recommendations for Introductory Machine Learning

Course recommendations for getting started with machine learning

Before you jump into deep learning, I would strongly advise you to do a few introductory machine learning courses to get up to speed with fundamental concepts like clustering, regression, evaluation metrics, etc. 

Here is a thread including a few recent courses you can explore:

This is a crosspost of a Twitter thread I published earlier this week.

---

Elements of AI

by University of Helsinki

Note: I have taken many machine learning courses online. I do some courses for fun but always learn something new. “Elements of AI” provides one of the most approachable, free, and fun AI courses I have taken. They added part 2 to practice building algorithms.

I suggest doing the first part, “Introduction to AI”. It introduces fundamental concepts like search, Bayes rule, nearest neighbor, and neural networks. There are some nice exercises all along the way. After the first course, you will have a good high-level picture of the field.

The second part (Building AI) provides the content for free but you need to pay if you want the certificate. I say it’s totally worth it! The second part is about implementing some of the basic algorithms (with Python) to understand concepts like optimization & the Bayes rule.

---

Create machine learning models

by Microsoft 

Note: The module on clustering is really good!

---

Stanford CS229: Machine Learning

by Stanford and Andrew Ng 

Note: One of my favorite ML courses of all time!

---

Machine Learning Crash Course

by Google 

Note: I took this course right when it was released and I was immediately hooked by the focus and high quality of it.

---

Introduction to Machine Learning for Coders

by Jeremy Howard 

Note: I have seen a few videos from the http://fast.ai courses and I can easily understand why their courses are so popular. Very hands-on approach! 

---

Foundations of Machine Learning

by Bloomberg ML EDU 

Note: If you love math and theory, you will like how deep this course gets.

---

Tabular Data

by Machine Learning University 

Note: This course touches on important machine learning topics at a high-level using easy to grasp explanations and examples of machine learning applications.

---

Stat 451: Intro to Machine Learning (Fall 2020)

by Sebastian Raschka

Note: Sebastian keeps adding awesome machine learning content to his YouTube channel and I really appreciate the content he puts together. Very approachable!

---

There are many other courses out there but I can only talk about the ones I have taken. Feel free to share (in the comment section) if you have found other good ones out there. It would be nice to share your experience with the course and why you like it or found it useful.

I will keep updating the list here as new and interesting courses emerge. 

Additional tips:

• Make a list of topics you found interesting & challenging

• Do more investigation on challenging topics 

• Practise coding 

• Share code 

• Write notes 

• Write/report on some interesting new result/idea you got 

• Take your time

• Engage in ML forums/discussions

I try to regularly maintain this guide. To get regular updates on new ML and NLP resources, follow me on Twitter.