A Definitive Guide on How to Become a Machine Learning Engineer

By Anchal Sethi


There are numerous creative ways in which machine learning is used behind the scenes to improve everyday lives. IBM’s Chef Watson or ‘smart’ sous chef, for instance, uses machine learning algorithms to help its human counterparts on food combinations to create entirely new flavours. In another example, manufacturers of Barbie have developed a new range of machine learning-powered dolls called “Hello Barbie”. This new range listens and responds to a child from 8,000 statements of dialogue stored on the servers. In the past few years, machine learning has evolved to be much more from just learning algorithms. It has branched out into a variety of niche applications like natural language processing, deep learning, recommendation engines, and reinforcement learning; and the resulting opportunities are nothing short of endless.

Machine learning is now used intrinsically not only in IT, but in consumer goods, retail, energy, creative arts, healthcare, manufacturing, financial services, media, finance, etc., in every imaginable way. So much so, that machine learning now is the reason behind companies churning out exceptional profits. For example, machine learning is such an integral part of Netflix’s video recommendation engine that the company has valued the ROI of these algorithms to stand at £1 billion a year.

Despite numerous innovation in artificial intelligence and machine learning, their involvement in businesses is only expected to grow. According to Monster, the job listings in machine learning have shown a consistent increase, thus making today as perfect a time as any to invest in machine learning courses.


Thus, machine learning is the next big thing in not just IT, but in almost every industrial sector. For those who are interested in pursuing a career in machine learning, here is a step-by-step guide on how to become a machine learning engineer:

Basics of R/Python:

While there are a multitude of languages that provide machine learning capabilities, most of the development work is carried out in “R” and “Python”. These are the most commonly used programming languages, and extensive community support is available on both. Before venturing into machine learning, it is essential to focus on either of the two. While both R and Python have their strength and weakness, an efficient machine learning engineer would gain most by diversifying his/her abilities to use whichever applies best in situations of predictive analysis and statistical modelling. That being said, irrespective of which tool you choose to begin with, the focus should always be on understanding the basics, followed by understanding its libraries and data structures.

Learn Descriptive and Inferential Statistics:

Since a variety of machine learning algorithms are based on statistical learning, it is always helpful for machine learning engineers to refresh their knowledge of statistics. To begin with, you can start with understanding the basics of inferential and descriptive statistics. There are many courses available on the web that explain the basics through Excel worksheets and assignments. For those intending to delve deeper into the subject, assignments on descriptive and inferential statistics can be practised using Python and R, and engineers can even refer to the respective methods and statistical libraries. This would help engineers in developing a clear understanding of how machine learning is used in tandem with statistical models.

Data Manipulation:

One of the qualities that differentiate a great machine learning professional from an average one is the ability to manipulate data in any manner possible. This is the skill of discerning which data points would render the required results, and the exploration, cleaning, and preparation of original data. While learning how to manipulate data is a time-consuming process, investing time in this aspect of machine learning would also help you with structuring machine learning algorithms. There is ample literature available on the internet that can be referred to learn more about the different stages of exploration. To go a step further, interested engineers can also refer to various data exploration methods in Python and R.

Enrol in a Machine Learning course:

Now that all the prerequisites are taken care of, the next step is to take up a formal machine learning course. A typical ML course would cover all the underlying algorithms, and would also introduce some popular new-age concepts like recommendation systems, neural networks, deep learning, and the application of machine learning in databases using Map Reduce. Once the basics are done with, the course structure would then cover the advanced machine learning techniques like deep learning, and using the algorithms to harness the benefits of big data.

Machine Learning libraries:

After building a good grasp on machine learning, the next step is to familiarise yourself with different machine learning frameworks and libraries. These libraries significantly simplify the process of data acquisition, building training models, and generating accurate predictions. Some of the most popular machine learning frameworks are Apache Singa, Amazon Machine Learning, Azure ML Studio, Caffe, H2O, MLib (Spark), Massive Online Analysis (MOA), mlpack, Pattern, Scikit-Learn, Shogun, TensorFlow, Theano, Torch, and Veles.

Roles and expected salary:

Skilled machine engineers can take up a number of job roles, including machine learning engineer, data scientist, data architect, cloud architect, data mining specialists, cybersecurity analysts, and many more. According to the recent industry estimates, the average salary of a machine learning engineer may vary from 8 to 15 lakhs per annum. An experienced professional with two to four years of experience could earn 15-20 lakhs per annum, whereas experienced professionals with 4-8 years of experience can earn between 8-12 lakhs per annum. Machine learning has defined the way in which businesses grow and interact with customers; however, its application is not limited to business alone. The sheer abundance of data and the need of personalisation has made machine learning a desired skill in almost every professional role. In a world which is increasingly being driven by data, machine learning is the perfect solution for anyone who wishes to stay relevant in their field.

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