You can use these courses for free

Artificial Intelligence is the future of the world. These days, every app, every website is employing AI for most of its functions. They are used for face recognition locks, to register and verify your security for transactions, and, of late, even put your face on various characters in gaming and non-gaming apps. Artificial IIntelligence makes it all possible and more. Artificial Intelligence is a useful skill to keep under your belt, especially since it was all the rage right now, as the dot-com boom a few years ago. Employers are looking for people with diverse skill sets, expressly those who can help their companies advance into the next generation.

Google – Machine Learning

This is a slightly more in-depth course from Google offered through Udacity. As such, it isn't aimed at complete novices and assumes some previous experience of machine learning, to the point where you are at least familiar with supervised learning methods. It focuses on deep learning, and the design of self-teaching systems that can learn from large, complex datasets. The course is aimed at those looking to put machine learning, neural network technology to work as data analysts, data scientists or machine learning engineers as well as enterprising individuals wanting to make use of the plethora of open-source libraries and materials available.

Stanford University – Machine Learning

This course is offered through Coursera and is taught by Andrew Ng, the founder of Google's deep learning research unit, Google Brain, and head of AI for Baidu. The entire course can be studied for free, although there is also the option of paying for certification which could certainly be useful if you plan to use your understanding of AI to increase your career prospects. The course covers the spectrum of real-world machine learning implementations from speech recognition and enhancing web search while going into technical depth with statistics topics such as linear regression, the backpropagation methods through which neural networks "learn", and a Matlab tutorial – one of the most widely used programming languages for probability-based AI tools.

Columbia University – Machine Learning

This course is also available in its entirety for free online, with an option to pay for certification should you need it. It promises to teach models, methods, and applications for solving real-world problems using probabilistic and non-probabilistic methods as well as supervised and unsupervised learning. To get the most out of the course you should expect to spend around eight to ten hours a week on the materials and exercises, over 12 weeks – but this is a free Ivy League-level education so you wouldn't expect it to be a breeze. It is offered through the non-profit edX online course provider, where it forms part of the artificial intelligence nanodegree.

Nvidia – Fundamentals of Deep Learning for Computer Vision

Computer vision is the AI sub-discipline of building computers that can "see" by processing visual information in the same way our brains do. As well as the technical fundamentals, it covers how to identify situations or problems which can benefit from the application of machines capable of object recognition and image classification. As a manufacturer of graphics processing units (GPUs), Nvidia unsurprisingly covers the crucial part these high-powered graphical engines, previously primarily aimed at displaying leading-edge images, has played in the widespread emergence of computer vision applications. The final assessment covers building and deploying a neural net application, and while the entire course can be studied at your own pace, you should expect to spend around eight hours on the material.

MIT – Deep Learning for Self-Driving Cars

As with the course above, MIT takes the approach of using one major real-world aspect of AI as a jumping-off point to explore the specific technologies involved. Self-driving cars which are widely expected to become a part of our everyday lives rely on AI to make sense of all of the data hitting the vehicle's array of sensors and safely navigate the roads. This involves teaching machines to interpret data from those sensors just as our own brains interpret signals from our eyes, ears, and touch. It covers the use of the MIT DeepTraffic simulator, which challenges students to teach a simulated car to drive as fast as possible along a busy road without colliding with other road users. This is a course taught at the brick's 'n' mortar university for the first-time last year, and all of the materials including lecture videos and exercises are available online – however, you won't be able to gain certification.