Learning Machine Learning: Where do I begin?

March 20, 2018 by Oskar Johnson Hägglund

The demand for AI talent is growing. How do I get started?

Finding the right AI talent is a challenge for companies of all sizes and demand is growing. LinkedIn’s 2017 U.S. Emerging Jobs Report shows that the number one fast-growing job is Machine Learning Engineer and the demand for Data Scientist roles have grown over 650% since 2012.

The next generation of data scientists and engineers are on their way and institutions are adapting to the future of work and new technologies but not as fast as market demand grows.

To introduce beginners to the concept of artificial intelligence, educational institutions and MOOCs are offering great online resources to introduce beginners to the concept of artificial intelligence and the subfield machine learning.

So where does one get started in a field that is constantly evolving? Here are a couple of resources introducing aspiring programmers and business professionals to a few core concepts and methods.

Take on the challenge and start digging into the exciting field of artificial intelligence!


Click the headers to reach the resources.

Deep learning for business professionals

Joong-Moon Chung from Yonsei University gives a practical introduction of AI from a business perspective with real industry examples of AI implementations and an introduction to how business professionals can make use of the deep learning.

Machine Learning: A Crash Course from Google

A great way to start exploring the concept of machine learning. Learn the philosophy behind machine learning and try out practical use-cases with the Google Tensorflow APIs in this series of lectures and exercises.

A visual introduction to machine learning

Using visual representations of datasets, R2D3 explains a machine learning model to distinguish New York homes from San Francisco homes.

Andrew Ng presents: A broad introduction to Machine Learning

Andrew Ng gives a broad introduction to machine learning, data mining and statistical pattern recognition. Learn techniques like computer vision, text understanding, and database mining.

Carnegie Mellon University: Machine Learning

Tom Mitchell from Carnegie Mellon University presents his lectures on machine learning techniques and classification algorithms.

MIT OpenCourseWare: Artificial Intelligence

MIT offers a free online course that introduces students to learning general methods of artificial intelligence and basic problem-solving. The course offers free video lectures, test assignments, and visual demonstrations.

Artificial intelligence from A to Z

Not free, but currently on sale. A guide to building your first AI with Python with no requirements of prior coding experience.

If you’re down for a more hands-on, applied, interactive and practical tutorials where you get to learn while building something close to the real world projects, the following might be for you:

Kaggle Courses

Free hands-on data science education from Kaggle – the world’s largest data science community. Educational tracks include Machine Learning, Deep Learning, and Data Visualization. Kaggle has other tutorials like this one https://www.kaggle.com/c/titanic where you will predict survival on the Titanic and get familiar with ML basics. Kaggle is definitely a place to explore and learn ML with hands-on practice.

Datacamp: Machine Learning with Python

Some content is free, while the full access requires a purchase. But there’s a clever workaround; by joining Visual Studio Dev Essentials program for free you can get a 2-month subscription to DataCamp for free and that, I guess should be sufficient time to complete the courses on machine learning.


A Practical Deep Learning course for coders. While the course itself is free, you’ll need a Nvidia GPU. If you have one already then it’s totally free. Otherwise, you’ll probably need to rent it from the cloud. The course itself is referring to and showing steps from Paperspace.

GitHub: Awesome Project Ideas

Lastly, after learning the ML with the help of tutorials and courses, you’ll want to put it into practice on your own. GitHub has “awesome project ideas” for you.


Bonus: Bayesian reasoning and machine learning

A free online book for graduate students, including a MATLAB toolbox and an engine for inference in probabilistic models.