What skills are required for machine-learning jobs?

https://onthe.io/learn/en/category/analytic/What-skills-are-required-for-machine---learning-jobs%3F

Machine
learning (ML) jobs require both a good understanding of how different kinds of methods work on historical data and how these methods can be practically implemented with various types of software. Of course, you can specialize in a certain subfield of ML
and you do not have to know everything about other subfields, but all the same, you should have quite a broad range of methods and techniques at hand to select optimal solutions to your problems.



Here are some important hints on what you need to learn:

Math basics and theory of algorithms. You should study probability theory and statistics as a great part of learning algorithms are based on probabilistic modelling. In addition,
it would be nice if you could learn convex optimization, quadratic programming, gradient decent and other applied math subjects
Although it is likely that you will not have to develop algorithms from scratch, it is very important to understand what theory lies in the basis of the methods you use. This
may turn out to be crucial to understand where or not you are on the right way

Distributed computing. Since ML often deals with big data you need to know the foundations of distributed computing that allows processing large amounts of data on several machines quickly and cost-effectively
Programming languages like Python, Java, C++, R. Developing applications for intelligent data analysis is an important part of ML, therefore you should develop the corresponding skills to become a pro
A great variety of ML algorithms. You need to understand how these algorithms work and what limitations they have. Also you should be able to select appropriate software tools and libraries to solve particular
ML problems
Remember that machine-learning jobs require the ability to make deep insights in data, select correct methods for specific tasks, and determine what works and what does not in particular cases. The ML methods are very
much problem-sensitive