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Machine Learning Engineer:
Data Engineer/Data Architect:
Data Scientist:
Data Analyst:
The Future of Machine Learning
Last Updated: Mar 27, 2024

Career Prospects After a Course on Machine Learning

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Machine Learning is emerging as one of the most sought-after career prospects today. It is likely to create 2.3 Mn. ML-related jobs by 2023 — according to a recent report published by Gartner. The Emerging Jobs Report released by LinkedIn shows that there are approximately 10 times more ML engineers today than five years ago. Artificial Intelligence, Data Science, Machine Learning, and all the other related technologies are some of the fastest-growing tech employment areas today.

The most in-demand ML jobs require you to research and develop algorithms that are used in adaptive systems — somewhat like Amazon and its recommendation systems. Companies recruit for positions like ML engineer, ML analyst,  Data science Lead, NLP Data Scientist, and ML Scientist.

Irrespective of the location you’re searching for a job in, chances are you’ll definitely find a well-paying Machine Learning job in most of them. Their salaries are reasonably good, too. And add that to the benefit of working in the field you love. From smartphones to chatbots, demand for Machine Learning jobs is at an all-time high, so it is just the right time to get in on the ground floor of a growing industry.

That’s precisely our topic for discussion today. Today, we’ll walk you through some of the best Machine Learning jobs out there for you after machine learning course online.

Machine Learning Engineer:

A Machine Learning engineer creates algorithms that help decipher information from huge chunks of data. ML engineers are supposed to be competent with Python, Java, Scala, C++, and JavaScript. They must be comfortable in building highly scalable systems that work on distributed networks. They should keep comfortable in working with teams and building personalized applications and algorithms. As a Machine Learning engineer, you’ll be designing and implementing ML algorithms such as clustering, classification, anomaly detection, or prediction — to address the challenge at hand and produce the best output.

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Data Engineer/Data Architect:

Data engineers have a completely different job role than ML engineers. As a Data Engineer, you’ll be responsible for working with an organization’s wholesome ecosystem. You’re recommended to have a working knowledge of Hadoop, MapReduce, Hive, MySQL, MongoDB, and Data Streaming. If you have a good knowledge of programming, you already have an added advantage in the job. In addition, you should be proficient in R, Ruby, Python, C++, Perl, and more.

If you work as a Data infrastructure engineer, you’ll be working with developing, constructing, testing, and maintaining highly scalable data management systems. You’ll develop custom applications that can perform analytics on heaps of data. You’ll also be responsible for collecting and storing data, doing real-time processing, and using it all to analyze the data via an API.

Data Scientist:

This is easily one of the most in-demand job professions in the tech. world today. Data scientists are, ideally, experts in languages like Python, R, SAS, MatLab, Hive, Pig, and more.

Being proficient in such Big Data technologies and analytical tools is a prerequisite for the job role. Simply because as a Data Scientist, you’ll be sifting through large amounts of unstructured data in order to derive insights and help come up with better future strategies. Other than that, you’ll also be required to clean, manage, and structure large chunks of data from disparate sources. This, too, requires the usage of sophisticated Big Data tools and technologies.

Data Analyst:

Data Analysts are persistent and passionate data miners who have a strong background in statistics, mathematics, and the basics of coding. The companies expect a data analyst to be familiar with data storing and retrieval systems, data visualization, Hadoop-based analytics, ETL tools for data warehousing, and other business intelligence concepts. The core responsibilities of a Data Analyst include designing, deploying, and maintaining algorithms, culling information and recognizing risks, extrapolating data using data modeling techniques, and pruning data.

Also Read, clustering in machine learning

The Future of Machine Learning

What we talked about are just the paths to get you started. And once you’re on it, there’ll just be growth, for the future of Machine Learning is extremely promising. There is, already, an urgent need for professionals who are not only trained well in Machine Learning but are also well-versed with the most important tools, techniques, and technologies. If you want to be one of those professionals, prepare yourself by diving deeper into machine learning and getting your hands dirty. That is the only way you’ll get deeper into what you learn and be able to prepare yourself for the future that lies.

Whether you’re a programmer, a maths graduate, or even a bachelor of Computer Science, you can land yourself any of these jobs with the relevant skills and knowledge.


These are the most sought-after jobs once you’ve successfully completed a course on Machine Learning and feel that you’re ready to face the job market. Reading through the article would’ve helped you narrow down the field of your interest. Once you’re there — dive deeper into precisely what does that job role entail, and get going! You can also consider our online coding courses such as the Data Science Course to give your career an edge over others.


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