Companies are today hard-pressed to find good machine learning talent, What they want from the pool of candidates, is one who already comes to the table equipped with the skill-sets, theories and coding ability needed for the task. The skill requirement is not only restricted to the knowledge of machine learning algorithms and when to apply what, but also how to integrate and interface. The core skills required are technical, with a good understanding of mathematics, analytical thinking and problem-solving. The theories of probability are the mainstays of most machine learning algorithms. If you are familiar with probability, you are equipped to deal with the uncertainty of data.
"Artificial intelligence will reach human levels by around 2029. Follow that out further to, say, 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold." Machine learning (ML) and artificial intelligence (AI) are ruling the digital world today. These technologies have the ability to completely transform the way a business operates. With the increasing adoption and business applications of AI and ML, many IT professionals are choosing AI and ML as their career.
The world is unquestionably changing in rapid and dramatic ways, and the demand for Machine Learning engineers is going to keep increasing exponentially. Now undoubtedly Machine Learning has arrived. To begin, there are two very important things that you should understand if you're considering a career as a Machine Learning engineer. You don't necessarily have to have a research or academic background. Second, it's not enough to have either software engineering or data science experience.
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.