Machine Learning: How to Become a Machine Learning Engineer?


The future, so glorified in movies, has already arrived, and things that until now seemed like science fiction have become reality. Many services use artificial intelligence in their work, for example, Netflix or online stores, which select the goods similar to those the user was searching for earlier. No modern company can do without Machine Learning engineers – especially since the trend of using this technology is gaining momentum. This job will not only provide you with a high salary, but it will also help shape your future.

If you’re thinking about what it takes to start down this path, there is no single way. However, often companies that pay good salaries require a bachelor’s degree. Of course, that will be a plus if you have experience in Software Engineering and data, and helpful languages for that would be C, C++, as well as Python, but again, all individually.

What does a machine learning engineer do?

A Machine Learning engineer’s job involves working with vast amounts of information, and the field focuses on developing self-directed software to automate predictions. He creates and implements a Machine Learning model, an algorithm that describes how the computer will learn, what data to use, what commands to execute, and in what order. To create a model suitable for business tasks, the engineer conducts experiments: trains the model on data, gives it tasks and checks how effective it is and whether it corresponds to the results given by the business.

Machine learning engineer skills, knowledge, and more

If you ask someone from this sphere what kind of navigational skills you need to become a specialist, you will get different answers, but Brights’ specialists have found the most general rules:

  • Math. First, a Machine Learning specialist must have a decent mathematical background. This is a need for a good understanding of how the classical Machine Learning algorithms work, and what happens inside modern neural networks.
  • R and Python. There are a lot of languages that are used for AI development, but these two are the most popular, with big communities, various frameworks, and libraries. You’ll need basic knowledge of programming basics so that you don’t get confused in your code at least, and better – quickly understand somebody else’s.
  • Courses, books, participation in Kaggle. Neural networks have gained leadership in modern Machine Learning, and now it makes sense to develop in this direction. A lot of books, free courses, and educational videos on Machine Learning and Data Science have appeared.

However, if you delve into such terms as Data Science and Software Engineering, you need special skills for each.

Data Science

Data Science is the generic name for the discipline of data aggregation, and Machine Learning is a division of Data Science that deals with building smart models. Such models can be used to predict user purchases, recommendations on social networks, image recognition, and so on.

In Data Science required:

  • Knowledge of programming languages
  • Ability to conduct hypothesis testing and develop evaluation strategies for predictive models and Machine Learning algorithms
  • Data modeling
  • Knowledge of not only mathematics but also statistics
  • Understanding of variance, correlations, and dynamic programming

Software engineering

In Software Engineering are often interested in such abilities:

  • understanding of data structures (heap, linked list, graphs, etc.)
  • ability to design systems
  • computer architecture knowledge
  • understanding of computability, complexity, and approximate algorithms

Are machine learning engineers in demand?

We certainly won’t amaze you, but it is one of the top IT jobs. This is not surprising, because Machine Learning is a growing area where companies are particularly eager to hire specialists because they can maximize the potential of artificial intelligence-based technologies that can operate in semi-automated mode. Companies are also starting to invest more and more in developing a set of new AI functions, especially to meet the needs of Machine Learning.

Can you learn machine learning without coding?

The answer is probably no rather than yes because confident basic programming skills are a must. Also, many professionals have a mathematical background, so they are better at some computer science topics than other programmers, such as algorithm analysis. Besides, for example, there is a problem that, if you don’t know how to code, you can’t solve:

Data pre-processing: you need to have good data in the model. If you don’t prepare the data well, the model can’t learn well – this is something that ML can’t do for you.

Career for specialists

If you have studied Machine Learning careers, your skills will be in demand in a variety of industries. For example, we can name positions in which this knowledge will be useful:

  • Machine Learning Engineer
  • NLP Scientist
  • Cloud Machine Learning architect
  • Data Engineer
  • Data Analysts

Although this is a tempting way to enter the field of IT, we still advise you to take into account all the pros and cons and make an algorithm of actions before you start working. Be patient – don’t expect quick results. Believe in your success and give yourself the strength to achieve your goals.

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By Sidharth

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