How Machine Learning is Changing the Job Market and Your Career Opportunities

Machine Learning as a career

Machine Learning is rapidly growing as one of the most exciting technologies of the 21st century. From face and voice recognition to self-driving cars, machine learning has made many things possible that might seem science fiction. All the leading enterprises are using ML algorithms to automate processes in their businesses. According to Fortune Business Insights, the machine learning market size is valued at $21.17 in 2022.

Undoubtedly, Machine Learning is changing the job market. But an important question that arises here is how? Is it only replacing human jobs or also creating new job opportunities? The demand for Machine Learning professionals is continuously rising in every industry. So, ML is also creating new career paths.

You can start with a machine learning certification to build a career in the machine learning field. You can also start looking at your options as a career path with machine learning certification as it evolves the job roles across industries.

What is Machine Learning?

Machine Learning is a subdomain of Artificial Intelligence. It allows machines to learn from past data and patterns to predict outcomes more accurately. It gives machines the ability to learn and build their programs to develop human-like behavior and decision-making skills. This is done without much human interference.

Instead of using explicit programming, the learning process is automated in these machines. By feeding good-quality data and using different algorithms, ML models are built to train machines to learn from this data.

Will Machine Learning Make Jobs Obsolete?

The global ML industry is expected to have a CAGR of 38.8% between 2022 and 2029. More than 82% of companies use Machine Learning models to increase productivity in their organizations. The incredible growth in the field has developed a fear of job obsolescence among the public.

Machine Learning is indeed making several human jobs less relevant through automation. But it is also creating new job opportunities at the same time. Companies need machine learning professionals to develop models and automate processes in their business. Moreover, human supervision is considered necessary even when most tasks are automated in an industry.

This suggests that current workers will not run out of jobs, but they might need to develop some skills. Around 73% of employers believe that Machine Learning has improved the productivity of their employees. So, companies might not fire their employees, but up-skill them to work with ML models.

Data Science vs Machine Learning

Data Science and Machine Learning, although related, are two different fields. Both are among the fastest-growing technologies today and are used in almost every industry. If you are interested in quantitative subjects like mathematics, statistics, programming, etc, you may be a good fit for either field. To choose a career, it is important to understand the relationship and the difference between the two terms.

Data Science is the study of data to extract meaning from it. On the other hand, Machine learning uses the extracted data to improve processes and predict results. Data Scientists use ML models to gather more information faster and ML professionals use data science techniques to make their machines learn about the data. This is how the two fields are closely related.

Let us understand the difference between data science and machine learning via the following table:

Data Science Machine Learning
  • Data Science is used to discover patterns in data using various statistical techniques to come up with a conclusion.
  • It involves three major processes: data collection, data cleaning, and data investigation.
  • It focuses on algorithm statistics as well as data processing.
  • ML is used to make machines learn by themselves using past data patterns.
  • It is of three types: Supervised, Unsupervised, and semi-supervised learning.
  • It is majorly focused only on algorithm statistics.

Machine Learning Introducing New Processes

Customer Service

Studies have shown that 81% of customers prefer self-service rather than speaking with a customer service agent. This is why companies are now improving their self-service offerings using ML-based customer service models, such as chatbots, virtual assistants, etc.

Recommendation Engines

ML-based Recommendation Engines use machine learning algorithms to segment customers based on their interests and buying behaviour. It further targets them by recommending the most relevant products and content suggestions. A leading e-commerce company recorded a 29% increase in sales due to ML-based recommendation systems.

HR & Legal Functions

ML algorithms can analyse large amounts of candidates’ data to identify the most suitable candidates. This helps HR professionals to make better data-driven decisions and hire potential candidates. Along with hiring, ML algorithms can be used in several other HR functions, including onboarding plans, documentation, etc.

Pharmaceutical and Healthcare Industries

Machine Learning has contributed significantly to the developments in healthcare during the last decade. Some of its most considerable applications in the pharmaceutical industry include:

  • Predicting disease: Based on the analysis of patient demographics and health records, ML models in healthcare can lead to accurate and faster disease prediction.
  • Drug Discovery: When launching a new drug in the market, ML models can reduce drug testing time and predict the drug’s impact and side effects.
  • Medical Imaging Diagnosis: Combined with deep learning and computer vision techniques, the ML image diagnostic tool is considered revolutionary in healthcare.

Also Read: 10 Reasons Data Science is the Most Respected Profession of the Future

Career Opportunities in Machine Learning

Machine Learning reduces human effort and improves machine performance by allowing them to learn on their own. To maximise the benefits of ML models, every company needs ML professionals. There are not just one or two but several career paths to choose from after completing a machine learning certification. Some most significant ones are listed below:

  • ML Engineer: A machine learning engineer creates ML algorithms that run on their own with minimal human intervention. Some major skills required to become an ML engineer are Statistics, Programming, Probability, Data Modelling, System Design, ML algorithms, etc.
  • Data Scientist: A data scientist is responsible for collecting, analysing, and interpreting large amounts of data using advanced technologies, such as Machine learning and Predictive Modelling. ML professionals can also become data scientists by opting for advanced data science certifications.
  • NLP Scientist: NLP (Natural Language Processing) is also a subfield of AI. NLP scientists work to make machines understand human languages. It required similar skills to an ML engineer.
  • Business Intelligence Developer: A business intelligence developer needs ML and data analytics skills to produce meaningful insights that can be used to make important business decisions.
  • ML Developer: A machine learning developer implements ML models into different software applications. They need to make sure that the algorithms and models are efficient, accurate, and can handle large amounts of data.
  • Research Scientist: A research scientist is responsible for researching and developing new ML technologies and algorithms. They can work in research institutions or in companies to build new products/services.
AI & Machine Learning Courses at Edvancer

Rather than opting for certification only in machine learning, you should prefer getting a machine learning and artificial intelligence course. It opens more career opportunities for you and helps you get higher-level jobs.

Edvancer has four types of AI and Machine Learning courses including:

These courses provide you with a comprehensive theoretical and practical understanding of AI and Machine Learning. You get the flexibility to choose a learning option based on your comfort and access the course from anywhere in the world.


1. What are the career opportunities after completing a machine learning course?
There are various career opportunities after completing a machine learning course. Some top career options are ML Engineer, NLP Scientist, Data Scientist, ML developer, and Research scientist.

2. How is Machine Learning changing the job market?
Machine Learning is changing the job market by automating various processes in different industries. It is making various jobs less relevant than others. However, it is also opening several new career paths.

3. How can I get started in a career in Machine Learning?
The best way to start building your career in Machine Learning is to opt for a relevant Machine Learning and Artificial Intelligence course. Get a course that provides you with theoretical as well as practical knowledge. The AI and ML courses at Edvancer are good options to check out.

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