If you aspire to become a machine learning engineer, you must first delve into the core concepts of machine learning.

What is Machine Learning?

One of the most striking contributions of technological development in the 21st century is the creation of Artificial Intelligence. Artificial Intelligence,, in turn, empowers the core of Machine Learning that serves humanity in the most advanced way. In other words, Machine learning is the way of predicting the most accurate outcomes using artificial intelligence on specific tasks and events within a software application. 

Who is a Machine Learning Engineer?

A machine learning engineer is one who plans, designs, and creates a system that runs on artificial intelligence to produce the most accurate predictive models. He works in the domain of the Information Technology hub in creating the most advanced self-running artificial intelligence system.

Roles of a Machine Learning Engineer 

• The preliminary data science information will be collected and processed, converting them into usable models. 

• Creating types of models that would recognize patterns within the collected and processed information or, put, design and build machine learning models

• Create artificial intelligence-imbibed applications that deem fit to the requirements of the industry.

• Verifying its effectiveness by conducting tests and experiments and fine-tuning, if necessary, using various statistical methods

• Retrain the machine learning model to hit accuracy and upgrade according to the latest developments in the field.

How to become a machine learning engineer?

Learn the programming language. 

The prior step towards reaching the goal of becoming a successful machine learning engineer is to grab the knowledge of programming languages. By doing so, you easily develop the first pillar to uphold your effectiveness as an engineer. 

Starting with the basics of python would be an intelligent decision. There are various resources available to guide you in learning the programming languages using python, which would help to understand the dynamics of structures, interfaces, database handling, and the fundamentals of data science. 

Sharpen Your Calculations

Statistics is going to be your loyal friend! You will need statistical ciphering like distribution, normal probability, standard deviation, and mean calculation, and accordingly, design your input features in the application or models. 

Knowledge of linear algebra, matrix, and probability will also be needed to decide well on the algorithms of machine learning. You get to learn all these in the Machine Learning stream in your Engineering college. It is important to select the stream from the beginning rather than switching it at a later stage. However, you may learn it posts doing your graduation in any other stream as well. 

Delve Deeper into the Concepts of Machine Learning

There are four basic types of machine learning algorithms. Namely - 

• Supervised 

• Semi-Supervised 

• Unsupervised

• Reinforcement learning. 

Gathering clarity in understanding the fundamental concepts of these four types would enable you to go deeper into the core concepts of machine learning and how it functions. 

Learn about the Data Science Tools 

Now to proceed further with the gathered knowledge from the above-mentioned steps, you must know more about the various data science tools that would help you create real-world projects. 

Using tools like pandas, NumPy, anaconda, etc., helps in understanding the various ways of data manipulation and analysis to work with the hidden patterns and design accordingly. To comprehend machine learning, one must unveil the trends of data in the best way possible. 

Working on Real-Life Projects

Now to put together every bit of your knowledge, you need some real experience that can be brought by working on real-life projects.

Final Thoughts 

There is ample scope for expansion in the sphere of machine learning. Following a course furnished by trusted institutions like JIET that would help you become what you aspire to be!