If you listen to the tech pundits (and many tech-firm CEOs), artificial intelligence (A.I.) and machine learning (ML) will define the future. And they’re certainly not wrong: virtually every industry is interested in how A.I. and ML can streamline business processes and boost profits.
These are clearly areas worth any tech professional’s time and attention. But for students and new graduates, the prospect of plunging into the study of artificial intelligence is no doubt intimidating; the associated technologies (not to mention the underlying mathematics) are often hideously complicated. Nonetheless, it’s important to familiarize yourself with A.I. and ML if you want to “future proof” your career.
If you’re totally new to the idea of A.I.—as in, the only thing you know about it is what you’ve seen in sci-fi movies such as “The Terminator”—that’s totally okay, because there are lots of resources out there that will give you basic-level instruction in A.I. theories and practice.
For instance, Hacker Noon offers a lovely breakdownof A.I. from a programmer’s perspective, including the industry’s “Holy Grail”: Artificial General Intelligence, or AGI (which some other resources call “General Artificial Intelligence”). KDNuggets also has a rundown of the basic terms and the technologies involved.
If you want to get to know some of the tools that actually make A.I. work, start off with Google’s three-hour introduction to deep-learning fundamentals. Since it’s Google, the materials inevitably focus on the company’s open-source software library, TensorFlow, that’s used in machine-learning applications (such as neural networks).
Google also offers a machine-learning “crash course” with 25 lessons and 40+ exercises, designed to take roughly 15 hours to complete.You don’t need to know a lot to start off with it, but it’s definitely a smoother process if you have some knowledge of programming basics, Python, and intro-level Algebra.
Once you’ve learned a bit about the particulars of artificial intelligence, check out IBM’s developerWorks, which includes a number of articles and tutorials on everything from neural networks to building Internet of Things (IoT) apps with Apple’s Swift and Watson (IBM’s A.I. platform). Of course, you have to keep in mind that IBM’s resources are slanted toward Watson, because the company wants developers to use its products; nonetheless, there is quite a bit of good material here about the fundamentals of A.I.
In a similar vein is Microsoft’s AI School, which offers lessons in everything from text analytics and object recognition to custom neural-network models. Yes, there’s an emphasis on Microsoft products, but there’s also quite a bit on “universal” A.I. skills. Take a look at this once you’ve learned some basic A.I. and machine learning concepts.
If you’re truly ready to get your hands dirty with artificial intelligence and machine learning, swing over to OpenAI, the kinda-nonprofit foundation (it’s complicated) devoted to creating an ethical framework for A.I. development. OpenAI hosts what it calls “Gym,” a toolkit for developing and comparing reinforcement algorithms, as well as a set of models and tools for training A.I. and ML. There’s also a handy, very extensive tutorial in deep reinforcement learning (i.e., deep RL).
OpenAI is likely something you won’t want to explore until you’re comfortable with the basics of artificial intelligence and machine learning, but it’s an excellent source of tools andresearch knowledge. Bookmark it.
If your mathematics knowledge is advanced (i.e., you’re deeply familiar with includes linear algebra, multivariate differential calculus, probability theory, and statistics) and you have a computer-science background, andyou’re intimate with data structures and algorithms, check out Bloomberg’s Foundations of Machine Learning, a free online course. It is, in a word, comprehensive.