Fundamental knowledge of the principles and practices of artificial intelligence (AI), automation and cognitive systems will become increasingly valuable in the future, regardless of business field, expertise or profession.
Luckily, today you don't need to spend years studying at a university to become familiar with this seemingly very complex technology. In recent years, a growing number of online courses have been available, covering everything from the basics to advanced implementations.
Some are aimed at people who want to get started quickly coding their own artificial neural networks and, naturally, assume some technical ability. Other courses are useful for anyone who wants to learn how to apply this technology to solve real-world problems, regardless of previous technical expertise.
In this post, we'll outline some of the best free tools available today.
Learn with Google AI
This newly published resource is part of Google's plan to broaden understanding of AI among the general public, and although materials are being added gradually, it already includes a crash course in machine learning using TensorFlow, Google's machine learning library.
This course will cover a basic introduction to machine learning, getting started with TensorFlow, and designing and training neural nets.
It is designed to help people with no machine learning knowledge get started quickly, to allow people with some experience to pick out the modules that interest them, and to serve as an introduction to TensorFlow for machine learning experts.
Google – Machine Learning
This is a slightly more in-depth course from Google through Udacity, so it's not for complete beginners and assumes you have some prior machine learning experience, at least some familiarity with supervised learning methods.
His focus is on deep learning and designing self-learning systems that can learn from large, complex datasets.
This course is aimed at enterprising individuals who want to leverage machine learning and neural network technologies as data analysts, data scientists, and machine learning engineers, and who want to take advantage of the many open source libraries and materials available.
Stanford University – Machine Learning
The course will be offered through Coursera and will be taught by Andrew Ng, founder of Google Brain, Google's deep learning research division, and head of AI at Baidu.
The entire course is free to study, but there is also the option to earn a certification for a fee, which will definitely come in handy if you are looking to leverage your understanding of AI to boost your career prospects.
The course covers a range of real-world machine learning implementations, such as speech recognition and enhancing web search, while also delving into technical details on statistical topics such as linear regression, the backpropagation method by which neural networks “learn,” and a tutorial on Matlab, one of the most widely used programming languages for probability-based AI tools.
Columbia University – Machine Learning
The course is completely free to take online, and you can also earn certification if you wish for a fee.
It aims to teach models, methods, and applications for solving real-world problems using probabilistic and non-probabilistic techniques, supervised and unsupervised learning.
To get the most out of the course, you should expect to spend around 8-10 hours per week over the course of 12 weeks working through the materials and exercises, but remember that this is free Ivy League-level education, so it won't be a cakewalk.
It will be offered through the non-profit edX online course provider and will form part of the Artificial Intelligence Nanodegree.
Nvidia – Deep Learning Foundations for Computer Vision
Computer vision is a subfield of AI that builds computers that can process visual information and “see” in the same way that the human brain does.
It covers not only the technical fundamentals but also how to identify situations and problems that would benefit from the application of machines capable of object recognition and image classification.
As a manufacturer of graphics processing units (GPUs), Nvidia is understandably responsible for the key role that these high-performance graphics engines, previously intended primarily for displaying cutting-edge images, have played in the widespread emergence of computer vision applications.
The final assessment will cover building and deploying a neural net application. The entire course can be completed at your own pace, but you should expect to spend approximately 8 hours working through the material.
MIT – Deep Learning for Self-Driving Cars
Similar to the courses mentioned above, MIT takes the approach of starting with one of the key real-world aspects of AI and exploring the specific technologies involved.
Self-driving cars, which are widely expected to become a part of our daily lives, rely on AI to make sense of all the data reaching the vehicle's sensor array and navigate our roads safely. This involves teaching machines to interpret the data from those sensors in the same way that the human brain interprets signals from our eyes, ears, and touch.
This course 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.
It's a course first offered at the university last year and all materials, including lecture videos and exercises, are available online, but it doesn't lead to a certification.
