What can you learn from Microsoft and AWS’ free machine learning courses?

According to LinkedIn, “Machine-learning engineer” was the fastest growing job category in the five years leading up to 2017. But tech’s hottest role isn’t a simple field to break into. It often requires a bachelor’s degree, or in some cases even a Masters or PHD, in a technical field.

Luckily there are an increasing number of options for those wanting to get a grounding in the field, with Amazon Web Services (AWS) being the latest tech giant to release a set of machine learning courses for free.

If you’re interested in these courses, it’s worth noting that you’ll benefit more if you have a basic knowledge of Python and GCSE Maths covering linear algebra, statistics, and calculus.


AWS offers over 30 online machine-learning courses, including video, labs and documentation, that have been used within Amazon for the past 20 years.  Developers can take courses that cover machine-learning building blocks through to how to build computer vision and natural language processing systems.  There courses are being offered as part of a new AWS certification in machine learning, which culminates in an exam designed to test your knowledge on how to carry out machine learning on the AWS platform. After completing the fundamentals, you will walk through real-world examples of applied machine learning, covering topics such as Amazon’s approach to delivery route optimisation.

How long does it take?

Many of the free online offerings are quite brief, consisting of videos that are at most a few hours long — although Amazon says there is more than 45-hours’ worth of material across the 30 courses.

The small print

To access these free courses, you will need an Amazon account and while the course material is free, the exam isn’t. Classroom sessions also come at a cost, with a one-day course on Deep Learning often costing around £500.


Microsoft’s main teaching program is its ‘Professional Program for Artificial Intelligence’ that’s offered on the edX learning platform.  Through a series of video lectures and coursework, the program aims to provide you with a grounding in machine learning; covering essential mathematics, how to use Python in data science, how to build machine-learning models, how to build functioning speech and computer vision systems, and other basics.  Towards the end of the course, you’ll be asked to solve a real-world problem using a deep-learning system Microsoft has developed.  Microsoft also provides a machine learning track under its AI School site, which offers 16 courses, many of which are focused on machine-learning services on Microsoft’s Azure cloud platform.

How long does it take?

Microsoft Professional Program for Artificial Intelligence – takes anywhere from between 120 and 480 hours. Each of the 10 courses run via edX for 3 months from January, April, July, and October throughout the year. Their ML Crash Course on the other hand takes just 13 hours.

The small print

The courses require both a Microsoft and edX account. It’s worth noting that like the AWS course, all the training material is free to access, but if you want the accreditation it will set you back between $99 — $990 for each course you complete – better get saving now!

So the big question is, can these courses help you to start or change your career?

Well, in short, yes and no. As you might have guessed, these courses aren’t necessarily the best way for someone without a technical background to break into a career as a data scientist or machine learning engineer. From a career perspective, these courses seem to be most useful for allowing anyone who already has a degree in a technical subject, such as maths, computer science or engineering to specialise and build on their technical foundations.

If these courses sound exciting to you, you can apply to any of Microsoft’s machine learning courses via  Microsoft’s AI School page or the Microsoft Professional Program for Artificial Intelligence site. Alternatively, if you’d like to access the AWS courses, then get started here.

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