Getting Started with Machine Learning ? 🤔
Now, I know some of you’re confused between Machine learning and Deep learning, what they exactly mean and where they are used? I am sure this short article will clear all your doubts -

Read it and come back here so that we can get started!

I’d like to point out that some people have a misconception that as Deep learning is a subset of Machine Learning so they need to learn traditional machine learning first as only after that they can move onto Deep learning, which is not true. You can get started with either of them as per your need.

Let us assume that you’re starting with traditional Machine learning first, here are few courses and playlists which you can find useful -

— Courses and Videos

1. Machine Learning (Stanford) by Andrew Ng.

It is 56-hour intro to ML course taught by Andrew Ng. He is one of the best teachers out there in the AI community. You can easily find more about him with just a google search. He goes deep in every topic that you will need to get started with ML, will make you great with basics and he is one of the most encouraging teachers you can ever have out there. Its curriculum is directly from Stanford’s Machine Learning course but he converted this to a MOOC version.

If you want to check out the course which was recorded back on 2009 in the class of Machine learning at Stanford then here’s the playlist -

He used Octave for solving the problems and assignments but it’s outdated now so I will suggest you to use this course for learning and as it has one of The Best content on machine learning and does the implementation part on Python. Here’s a Github repo which can help you out on this —

https://github.com/dibgerge/ml-coursera-python-assignments.

2. Intro to ML for coders by Jeremy Howard.

This course is based on lessons recorded at the University of San Francisco for the Masters of Science in Data Science program.

It is taught by Jeremy Howard, his courses are special because he uses a top-to-bottom approach to teach where he will show what a particular thing can do and introduce you to all concepts and then slowly deep dive into them making you understand all the basics of it. So if you think that you will learn the best at that kind of setting then be sure to check this out!

3. Applied Machine Learning in Python (University of Michigan).

It is also a great course offered by University of Michigan, it uses the scikit-learn library in Python to discuss the Machine Learning concepts.

4. Kaggle

This is one of the best sites out there to gain experience on how to solve real-world problems as it organizes many ML related competitions and there are thousands of public datasets available which you can use and can also upload your datasets. Or train, test and publish your working inside Kaggle. It also has an amazing community which is super active and contains some of the amazing resources for anyone be it a beginner or a pro in Data science.

It also has great short courses to get you started to be it Python, Machine learning or Deep learning -

And here’s the getting started discussion forum of Kaggle which has numerous amazing resources :)
['Biswaroop Bhattacharjee']
['Getting Started', 'AI', 'Computer Science', 'Machine Learning', 'Guides And Tutorials']