Who is who in ML and how to start your career in machine learning

The most common definition of machine learning is the ability of computers to learn from data without explicit programming.

If we compare ML with traditional programming, we can say that with ML, we enable computers to identify certain patterns in data and create forecasts based on them without having to get exact instructions on what to look for.

Who is a machine learning specialist?

A machine learning programmer is a specialist who trains artificial intelligence using special algorithms and data sets. If you need professional ML programming support, check this company services. They are among the top experts based on independent researches, like Clutch. I worked with them for some time and was impressed by their professionalism.

In the scientific field ML specialist is working with the analysis of large amounts of data and use in the work mathematics, ML SaaS platforms, frameworks, and such programming languages as Python., R, MatLab.

Why Python?

Python is a high-level programming language that is used in various areas of IT, such as application development, web programming, server work automation, and others. Python is widely used in ML. And here’s why:

  1. Its syntax is simple and clear, there are many frameworks, specially created for work with the large masses of data.

An example of an ML project in Python: Video Object Removal is a program that uses deep learning algorithms to remove any unnecessary objects from a video in just a couple of seconds.

 Certainly, Python is not the only program language that can be used for machine learning. Recently I have had the opportunity to chat with engineering talents who started their career as a data science engineer or were planning to do so. Many of them have no programming experience. What I noticed was that people from the field of statistics also had enough simplicity to write algorithms. They were more satisfied with R. R is the Project for Statistical Computing).  Currently, R is the second best language chosen by the industry for data science. I personally find Python better and always advise you to learn it. Obviously, you don’t need to know everything in Python.

There is another argument why you don’t have to limit yourself with only Python program language. Many of you must have heard about low-code/no-code apps. In a business society  with increasing competition in the fight for the customer’s love, low-code/no-code application can help a lot. The reason why is the simple and convenient interfaces, which allows marketers and business owners to visualize the trends and to set the cohorts without intermediate specialists. Within such Saas you can use pretrained models to work with you own data and receive the answers/consepts and check the gipothesis without great investment.
The point here is not that everyone wants to save on the salary of the CRM/ERP system vendor, but that low-code / no-code gives great advantages in terms of the cost of owning the system, the cost of changing the system and the cost of fixing a system’s error.

The essence of low-code/no-code is to reduce the threshold for creating/changing an information system to the level of a business analyst or even an advanced user. This is when the vendor does not just create a platform with a built-in language, but when business analysts or dedicated responsible people on the client’s side can do almost everything themselves.

More and more successful no-code projects are entering the global market, which receive funding from large investors. Over the past five years, at least two giants have appeared in this area: Airtable with an estimated $1.1 billion and Notion with an estimated $2 billion


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