Don't know where or how to start learning? But learning more about artificial intelligence, and the myriad overlapping and related fields and application domains does not require a PhD. Getting started can be intimidating, but don't be discouraged; check out this motivating and inspirational post, the author of which went from little understanding of machine learning to actively and effectively utilizing techniques in their job within a year. With more and more institutes of higher learning today making the decision to allow course materials to be openly accessible to non-students via the magic of the web, all of a sudden a pseudo-university course experience can be had by almost anyone, anywhere. Have a look at the following free course materials, all of which are appropriate for an introductory level of AI understanding, some of which also cover niche application concepts and material.
Most of the Machine Learning, Deep Learning, Computer Vision, NLP job positions, or in general every Artificial Intelligence (AI) job position requires you to have at least a bachelor's degree in Computer Science, Electrical Engineering, or some similar field. If your degree comes from some of the world's best universities than your chances might be higher in beating the competition on your job interview. But looking realistically, not most of the people can afford to go to the top universities in the world simply because not most of us are geniuses and don't have thousands of dollars, or come from some poor country (like we do). No with the high demand of skilled professionals from these fields, there are exceptions being made, so we can see that people who don't come from these fields, are learning and adjusting themselves in order to get that paycheck. In this article, we are going to list some of the free Artificial Intelligence courses that come from Harvard University, MIT University, and Stanford University that anyone can attend, no matter where they live.
One of the most acclaimed courses on using deep learning techniques for natural language processing is freely available online. To be clear, this isn't a recent occurrence; Stanford's Natural Language Processing with Deep Learning (CS224n) materials have been available online for quite some time, years in fact, and the available materials are constantly being updated to closely reflect what the in-school course looks like at any given time. And to be even more clear, there is no option to enroll, as this is not a MOOC; it is simply the freely available materials from this world-class course on the topic of deep learning with NLP. First, to provide clarity, here is the course's self-description: Natural language processing (NLP) or computational linguistics is one of the most important technologies of the information age. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertising, emails, customer service, language translation, virtual agents, medical reports, etc.
This is a collection of free resources beyond the regularly shared books, MOOCs, and courses, mostly from over the past year. They start from zero and progress accordingly, and are suitable for individuals looking to pick up some of the basic ideas, before hopefully branching out further (see the final 2 resources listed below for more on that). These resources are not presented in any particular order, so feel free to pursue those which look most enticing to you. All credit goes the the individual authors of the respective materials, without whose hard work we would not have the benefit of learning from such great content.
Machine learning technology has the capacity to autonomously identify malignant tumors, pilot Teslas and subtitle videos in real time. The term "autonomous" is tricky here, because machine learning still requires a lot of human ingenuity to get these jobs done. It works like this: An algorithm scans a massive dataset. Engineers don't tell it exactly what to look for in this initial dataset, which could consist of images, audio clips, emails and more. Instead, the algorithm conducts a freeform analysis.