With Coronavirus on the prowl, there has been a huge demand across the world for MOOCs as schools and universities continue to shut down.
So, I find it great that providers like Coursera are hosting a lot of excellent courses on their site for free, but they are a little hard to find among all the paid courses.
While these courses are not providing verified certificates if you take them for free, in my view, it is the knowledge that matters than having a few certifications.
TLDR; With thousands of individuals laid off from this crisis, I believe it is crucial to get learning resources out now to people. So here is a list of courses that are great and free to learn.
1. Machine Learning
Yes, you heard it right, Coursera is providing the Game Changer Machine Learning course by Andrew Ng for free right now.
As for my review, I think this is the one course that should be done by everyone interested in Machine Learning. For one, it contains the maths behind many of the Machine Learning algorithms and secondly Andrew Ng is a great instructor. Believe it or not, Andrew Ng not only taught but also motivated me to learn data science when I first started.
As for the curriculum, this course has a little of everything — Regression, Classification, Anomaly Detection, Recommender systems, Neural networks, plus a lot of great advice.
You might also want to go through a few of my posts while going through this course:
Algorithms and data structures are an integral part of data science. While most of us data scientists don’t take a proper algorithms course while studying, they are essential all the same.
Many companies ask data structures and algorithms as part of their interview process for hiring data scientists.
They will require the same zeal to crack as your Data Science interviews, and thus, you might want to give some time for the study of algorithms and Data structure and algorithms questions.
This series of two courses offered by Robert Sedgewick covers all the essential algorithms and data structures. The first part of this course covers the elementary data structures, sorting, and searching algorithms, while the second part focuses on the graph and string-processing algorithms.
You might also like to look at a few of my posts while trying to understand some of the material in these courses.
- 3 Programming concepts for Data Scientists
- A simple introduction to Linked Lists for Data Scientists
- Dynamic Programming for Data Scientists
3. Bayesian Statistics: From Concept to Data Analysis
“Facts are stubborn things, but statistics are pliable.” ― Mark Twain
The war between a frequentist and bayesian is never over.
In this course, you will learn about MLE, priors, posteriors, conjugate priors, and a whole lot of other practical scenarios where we can use Bayesian Statistics. All in all, a well-packaged course which explains both frequentist and bayesian approach to statistics.
From the course website:
This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach.
4. Practical Time Series Analysis
Have you heard about ARIMA models, Stationarity in time series, etc. and have been boggled by these terms? This course aims to teach Time series from a fairly mathematical perspective. I was not able to find such a course for a fairly long time. And now it is free for all.
From the course website:
In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data
If you want to use XGBoost or Tree-based models for time series analysis, do take a look at one of my previous post here:
5. Getting Started with AWS for Machine Learning
The secret: it’s not what you know, it’s what you show.
There are a lot of things to consider while building a great machine learning system. But often it happens that we, as data scientists, only worry about certain parts of the project.
But do we ever think about how we will deploy our models once we have them?
I have seen a lot of ML projects, and a lot of them are doomed to fail as they don’t have a set plan for production from the onset.
Having a good platform and understanding how that platform deploys machine Learning apps will make all the difference in the real world. This course on AWS for implementing Machine Learning applications promises just that.
This course will teach you:
1. How to build, train and deploy a model using Amazon SageMaker with built-in algorithms and Jupyter Notebook instance.
2. How to build intelligent applications using Amazon AI services like Amazon Comprehend, Amazon Rekognition, Amazon Translate and others.
You might also look at this post of mine, where I try to talk about apps and explain how to plan for Production.
- How to write Web apps using simple Python for Data Scientists?
- How to Deploy a Streamlit App using an Amazon Free ec2 instance?
- Take your Machine Learning Models to Production with these 5 simple steps
More Free Courses
Also, don’t worry if you don’t want to learn the above ones. I have collected a list of some highly-rated courses that are free to audit before writing this post. You can download the excel file here. So have a stab at whatever you want to learn.
I am going to be writing more beginner-friendly posts in the future too. Follow me up at Medium or Subscribe to my blog to be informed about them. As always, I welcome feedback and constructive criticism and can be reached on Twitter @mlwhiz.
Also, a small disclaimer — There might be some affiliate links in this post to relevant resources, as sharing knowledge is never a bad idea.