How Can You Pursue a Career in Machine Learning: A Comprehensive Guide

How Can You Pursue a Career in Machine Learning: A Comprehensive Guide | Artificial Intelligence and Machine Learning | Emeritus

Did you know that there are currently 1.1145 trillion megabytes (MB) of data generated daily? Managing large-scale data is becoming an increasingly challenging task, making the machine learning career path particularly meaningful for professionals interested in monitoring data and working with algorithms. This is especially because, over the past decade, Machine Learning (ML) practices have achieved enough effectiveness for large-scale adoption across many industries. Consider that, according to Fortune Business Insights, the machine learning market size is projected to grow from $21.17 billion to $209.91 billion between 2022 to 2029 at a Compound Annual Growth Rate (CAGR) of 38.8%. 

There can be multiple paths to building a successful career in this ever-evolving field. How do you figure out which one is right for you? We’ll discuss that in this blog but, first, let’s summarize what machine learning is. 

What is Machine Learning?

In simple words, machine learning enables systems to learn from experience without having to be explicitly programmed. It uses algorithms to identify patterns, analyze data points and transform them into useful functionalities. The process begins by feeding automated data into a machine and training it to derive specific results by applying different ML models and algorithms. While most organizations are still in the early stages of adapting these technologies at an enterprise level, it has led to a growing demand for professionals in this field.  

Now, let’s explore what an ideal machine learning career path looks like. 

ALSO READ: What is a Machine Learning Model? How is it Helpful?

What is the Machine Learning Career Path?

There is no one–size–fits–all approach to landing a job in the machine learning industry. Due to its high level of complexity, the expectations from candidates for these roles are greater than those for other IT profiles. 

Typically, a machine learning professional will have a bachelor’s degree in computer science, software engineering, statistics, or deep learning. 

On the other hand, if you are looking for a mid-career switch or transition to a machine learning career, consider part-time internships, coding bootcamps, short-term training programs, certified online machine learning courses, or follow a self-designed learning path to get familiar with essential concepts. 

Here are some common machine learning career paths to consider: 

Machine Learning Engineer

A machine learning engineer creates machine learning models and develops the most effective algorithms using statistical analysis. They should be well-versed in programming languages such as Python, Scala, and Java, as well as system design and data modeling. Individuals with experience in analysis, statistics, or problem-solving models are suitable for this role.

Data Scientist

This is among the most popular jobs for professionals working with machine learning. They are essentially experts who find patterns in large data sets and convert them into formats for machine learning models to interpret. This role demands years of experience in data analysis and data engineering. 

Natural Language Processing Engineer

A Natural Language Processing (NLP) engineer is responsible for developing software and technology to learn human speech patterns and translate them into formats the machine learning model can understand. They are required to be fluent in spelling and grammar, syntax reading, method extraction, sequential modeling, and text processing. These niche prerequisites are the reason why there are very few skilled NLP engineers compared to other machine learning disciplines. 

Business Intelligence Developer

They are professionals who collect, evaluate and interpret large amounts of data to produce usable insights that drive better business decisions. They need to have knowledge of both multidimensional databases as well as programming languages such as Perl, Python, and SQL. Working knowledge of business analytics services is a plus.

ALSO READ: 4 Types of Machine Learning for a Great Career in Tech

Certifications that Can Help

Certification can prove very important in shaping your machine learning career path. If you already have a degree in a related field, a certification not only recognizes your qualifications but also adds credibility to your overall profile. Theoretical knowledge apart, it is also essential to have the applied skills required for a machine learning role. 

A certification program is a great way to master the skill sets needed to help you land your dream role in the industry. For that, you can explore the artificial intelligence and machine learning courses by Emeritus. 

ALSO READ: The 7 Best Machine Learning Courses on The Web Today

What are the Top Skills in Demand?

A key aspect of building a career in machine learning is knowing programming languages like Python, C++, Java, and R, mathematical knowledge, and data visualization techniques. Apart from these, here are other must-have skills for every machine learning professional:

Data Modeling and Evaluation

In order to maximize the efficiency of machine learning, it is important to evaluate the effectiveness of various models using regression or classification at regular intervals. It helps identify errors and check the accuracy of any given model.

Statistics and Probability

A good understanding of algorithms is linked with your knowledge of theories such as the Hidden Markov models, Gaussian Mixture, and Naive Bayes, among others. You must be adept with concepts of statistics and probability to grasp these algorithms with ease. 

Signal Processing

As a machine learning professional, you may work with many advanced signal processing algorithms like shearlets, contourlets, curvelets, and more. Since feature extraction is a key element of machine learning, knowledge of signal processing techniques is necessary.

Machine Learning Algorithms

Learning how different algorithms work in different situations is important. Gradient descent, partial differential equations, quadratic programming, and convex optimization are some of the key concepts you need to know.

Average Salary across Roles

Job Title Average Annual Salary
Machine Learning Engineer $150,813
Data Scientist  $124,687
NLP Engineer $140,000
Business Intelligence Developer $94,232
Automation Engineer $95,269
Data Engineer $128,631
Deep Learning Engineer $141,548

*All salaries are as per reports by Indeed and from to the U.S. market

Top Companies looking for Machine Learning Experts

According to Glassdoor, here is a list of the most popular companies in the United States that hire machine learning professionals: 

  • Apple
  • Google
  • Meta
  • LinkedIn
  • Intel Corporation
  • Amazon
  • JP Morgan Chase & Co.
  • Capital One
  • Lockheed Martin
  • IQVIA 

Given how rapidly this industry that is growing, it is no surprise that employers are eager to recruit experts in the machine learning field. To build and grow your machine learning career, upskill yourself with the machine learning courses on the Emeritus platform, created in collaboration with top global universities.

Write to us content@emeritus.org

Machine Learning Career

About the Author

Content Contributor, Emeritus
Siddhesh is a skilled and versatile content professional with 4+ years of experience in writing for the digital space and the screen. As a polyglot with a flair for many different languages, he specializes in creating engaging narratives. With a passion for storytelling and an unwavering commitment to excellence, he writes thought-provoking and persuasive blogs about careers in different fields. Siddhesh is a doting cat parent and has also graduated to becoming a musician after releasing his debut single on Spotify recently.
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