It’s no secret that machine learning skills are high in demand. 25% of the world’s jobs are expected to be automated in the next ten years, so someone must take the responsibility of teaching them. Companies are constantly on the look out for professionals who have the skills to help them use machines for tasks – simple and complex – for various purposes. Here’s a low down on the most in-demand skills that machine learning experts possess.
In-Demand Machine Learning Skills
If you must learn, learn deeply. The rule applies to humans and machines. Although, machines are better at deep learning. Artificial Neural Networks (ANNs) are far more adept at processing complex voluminous data than us mortals. Programs like Google’s TensorFlow are used to improve healthcare, increase agriculture yield and even combat climate change.
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The English writer Jeanette Winterson once said that language is freedom because the moment you find the words to express yourself, you become comprehensible. But what if you’re talk to a rock? Well it would be the same as talking to a computer without Natural Language Processing (NLP). Now you know why teaching computers how to interpret human language is so very important.
Short for Robotic Process Automation are skills that allow us to automate moronic routine tasks. The market for products and services that do this was worth $ 271 million in 2016 and is expected to rise to $ 1.2 billion by 2021. Who’s moronic now?
This skill shouldn’t be this low in this list honestly. But it’s been overstated, and everyone knows it. Python, R, Java, C++ – the more languages you know, the better you will be. Because programs need programming. And programming needs programming languages. It’s obvious really.
Probability & Statistics
Machine learning is anticipatory. Its objective is to enable machines to sense possibilities and determine the most probable outcomes and expected responses to those outcomes. This would be hard to achieve without probability and statistics. I’d say probably impossible.
Probability is calculated. And calculations, as you learned in school, use math. By logical derivation, it is safe to conclude that machine learning involves applied math. See how I added points 1 and 2?
We use machines because they can learn from large and complex data sets. But extremely large volumes can be overwhelming even for machines. Hence, it is necessary to distribute their computation power across machines for better results. That’s why skills in programs like Apache Hadoop and Spark that enable distributed computing are valuable.
Machine learning is increasingly implemented across industries and divisions. In the finance industry it is used to increase the speed of transactions. In the HR division, it is used to automate routine tasks like scheduling interviews. Machine learning has a variety of functions and the demand for machine learning skills is rapidly increasing. Both startups and big companies are looking to use machine learning and artificial intelligence for various business and organizational purposes. If you’re looking to establish yourself as an expert in machine learning, then it might be wise to master these in-demand machine learning skills.