The right foundation to start a career in machine learning

Let us establish what a machine learning engineer usually does before we get into the primary question. If the notion of machine learning creates an image of intelligent robotic devices that behave like human beings in your mind, you need to calibrate that image a little bit. A machine learning engineer is essentially a problem solver. As a ML professional your day-to-day task is to use data to identify, understand, and address business problems.

You engage in enhancing marketing efforts; create a production road map; find out ways of optimizing different business functions. Then you tweak these systems to keep them current. What makes your role very special is the way you perform your tasks – by training algorithms to draw insights from data.

You do not need a PhD

If you have one, that is great. If you do not, it does not matter. We keep carrying the notion that every data science or ML professional should have a doctoral degree while common developers win the DeepRace organized by AWS. No progressive employer is after a PhD. With that off the cards, let us look at some things that you do need.

A stronghold in mathematics

You cannot make any large strives in machine learning without a working knowledge of linear algebra, probability and statistics, and calculus. The two key concepts of linear algebra are matrices and vectors. You need matrices to perform various tasks including image recognition, where the image already exists in the form of a matrix. The recommendation systems on Amazon and NetFlix are based on Vectors. Similarly the concepts of probability and calculus are equally important for the foundation of your career in best machine learning course.

Programming skills

Python and R have dominated the list of the most popular languages for data science and machine learning for years with Python taking a lead at the moment. People use C and Java Script for machine learning algorithms but R and Python have some clear advantages in terms of brevity of code and availability of libraries. Your programming skills will determine your autonomy as a machine learning expert.

Data engineering skills

We can divide this in two parts – data pre-processing and database management. As a machine learning engineer you will need to be able to clean, parse, and prepare the data for your machine learning algorithms. This also involves ETL or Extract, Transform, Load. You need to learn to extract data from different sources; transform it into formats that your algorithms would accept. Some DBMS skills also go a long way in machine learning.

Finding the best machine learning course

You need to find the course that suits you and your goals. Getting into the right institution for the apt course is crucial in terms of your career. An institute with a solid track record, successful alumni, and expert teachers is what you need to gain a solid foundation.  Add a good community of users that can help you get around some issues, and you have the perfect start to your journey.