If you're at all interested in Artificial Intelligence (AI), it's unlikely to be news to you that there is an AI skills shortage. Businesses are increasingly looking to invest in AI and are on the hunt for suitably skilled workers since traditional software teams without the experience of AI often encounter a number of challenges, as I described in a recent article over on DZone. Anyone thinking about joining the AI workforce will want to learn the subject, initially by doing some reading and research, but without committing to paying too much. As the need to recruit skilled AI staff has grown, so a number of businesses and individuals have set out to provide training courses, books, and e-learning, and the price and quality of these vary, as you would expect. As with all education, if you commit a chunk of your time, you don't want to find it wasted on out-of-date or incorrect information or to find that you are missing out on key skills after spending time and money on a course that promises to equip you appropriately.
When the world's smartest companies such as Microsoft, Google, Alphabet Inc., and Baidu are investing heavily in Artificial Intelligence (AI), the world is going to sit up and take notice. Chinese Internet giant Baidu spent USD1.5 billion on research and development. And as proof of China's strong focus on AI and Machine Learning, Sinovation Ventures, a venture capital firm, invested USD0.1 billion in "25 AI-related startups" in the last three years in China and the U.S. Research shows that although genuine intelligence may still be a bit far off, AI and Machine Learning technologies are still expected to reign in 2017. Try reading up on Microsoft Project Oxford, IBM Watson, Google Deep Mind, and Baidu Minwa, and you'll understand what I am trying to get at. In 2015, Gartner's Hype Cycle for Emerging Technologies introduced Machine Learning (ML), and the graph showed (Figure 1) that it would reach a plateau in 2 to 5 years.
If the math behind data science is an enigma, the Khan Academy is a great place for insight. There are courses for different levels, and Sal Khan's relaxed delivery will get you through even the most difficult concepts (I think I have a small crush on him after the hours I've spent listening to his narratives!).
Hi, I'm planning to make a 5/6 part series to reflect about my experience in University of Washington Machine Learning Specialization in Coursera while I take the five courses: Foundations, Regression, Classification, Clustering, Deep Learning and finish the capstone project. This is the first article in the series. I'll feature the first course Machine Learning Foundations: A Case Study Approach in this article and describe the philosophy behind the'case study approach' with a brief overview of the tools used and reflect on what I've learnt. I hope it will help people who want to use the same specialization.I'm also taking courses in Udacity, Edx and using other resources too, but experience those resources will be described in separate articles. I'm also planning to write a whole separate series on Udacity Machine Learning Nanodegree in recent future.