Machine learning is one of the most high-paid and prestigious domains nowadays. There is no wonder that many people, young and old, are interested to know more about the profession of a machine learning engineer.
An ML specialist is a programmer who teaches the machines to reason on their own. This technology has many practical applications that we all benefit from. Face or fingerprint recognition system in your phone, climate control in your car, Siri and Alexa, Netflix with its recommendations — these are all the results of the work of machine learning specialists.
ML specialists work with large amounts of data, trying to extract useful information. They help to answer any questions, for example, why one manager closes more deals than the other, how many units of the product need to be purchased in the next quarter, and which components of the drug will improve the patient’s well-being. To solve some problems, specialists develop algorithms that are able to generate a result even without human intervention.
So what are the skills that you need to get started? Read this post to find out. And check these ML tools to understand how machine learning scientists do their job.
Table of Contents
You can do Data Science without deep knowledge of fundamental mathematics: modern libraries contain a huge number of ready-made solutions. With their help, you can analyze data and train algorithms without going into mathematical details. But only until you encounter the first really difficult or atypical task.
It is only possible to deal with them if the data analyst really understands how all the lines of code work “under the hood” in terms of mathematics and statistics. Therefore, large companies in interviews often check the level of knowledge of the applicant in these areas. Among the particular areas of focus are linear algebra, mathematical analysis, statistics, and probability theory.
The most popular and widely used language in Data Science today is Python. Before it, the most popular language was R, which is still used for data analysis, scientific statistical analysis, and sociology.
Among other things, Python is good because it helps you develop almost any solution. It has a large variety of rich libraries specifically for ML. Python is distributed freely and is easy to install (you can even work with the IDE in your browser).
For data to be processed by the machine, it must have a structure in the analytical, informational, and even physical meaning of this word. It also needs to be stored somewhere in a proper form. Organizing work with data measured in hundreds and thousands of terabytes is not easy. There are special approaches, concepts, and tools for interacting with it that you will need to master.
To become a data scientist, you need to master many skills in a wide variety of areas. This is a feasible task: each area can be mastered and deepened gradually.
Future data analysts need self-organization and dedication. You will have to navigate the flows of information, not to get lost in the most relevant methods and principles, and most importantly – figure out why something is or is not working well.