Machine Learning
Explore practical applications of machine learning in business and gain new, cutting-edge technical skills. Find online machine learning courses on GetSmarter, offered in partnership with top universities and institutions.
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The sheer range of machine learning’s practical applications make the field both rapidly expanding and largely unavoidable in business. Whether it’s the recommended TV shows on a streaming service, autonomous vehicles on the streets, or the fraud detection services used by your bank, machine learning (ML) is everywhere. The global machine learning market is projected to grow from $26 billion in 2023 to about $226 billion by 2030.1
With machine learning online courses, professionals can explore this developing field and gain technical skills quickly.
What is machine learning?
Machine learning is a subfield of artificial intelligence (AI) that uses algorithms and data exposure to teach computers to make predictions.2
Traditional programming teaches computers via detailed instructions, comparable to a baking recipe that lists the exact ingredients and process for someone to follow. In machine learning (ML), computers learn via experience — a model trains itself on data to find patterns. With more data, the program becomes more accurate at its task.3
There are three primary types of machine learning. Supervised machine learning uses labeled data to train a model to achieve a known, desired output. In unsupervised machine learning, unlabeled data is used to help the model discover patterns without explicit instructions.4 Reinforcement learning uses a repeating system of trial and error so the machine learns from past experiences.5
Machine learning methods like computer vision, automatic speech recognition, and natural language processing are applied to all kinds of data, from audio and image files to massive spreadsheets. In turn, ML can be applied to a variety of use cases.
Some real-world examples of ML include:
- Recommendation systems
- Autonomous vehicles
- Fraud detection
- Chatbots
- Voice assistants
Jobs in machine learning
Machine learning is a varied research field; some careers are perfect for people who want to execute big ideas and there are others that are focused on a specific facet of the ML lifecycle. Often, ML could be just one part of how a professional solves a technical problem, operationalizes their goals, or builds a product.
Some examples of job titles that often use machine learning knowledge include:
- Machine learning engineer: Researches, builds, and designs AI systems to automate predictive models6
- Data scientist: Uses analytical tools like programming and data visualization to extract meaningful insights from data. These professionals can also develop machine learning algorithms7
- Data analyst: Collects, processes, and analyzes data from various sources to help achieve business goals8
- Data engineer: Builds and maintains an enterprise’s data infrastructure. These professionals can automate data integration and manage the data pipeline9
- Database developer: Designs, builds, tests, and maintains an organization’s databases. These professionals use programming languages to modify data structures and keep data secure10
Why should I enroll in machine learning short courses?
When you study machine learning, you gain skills in a rapidly evolving technology that influences every industry. Experienced leaders can explore how AI can work for their organization and study practical applications of machine learning in business. Alternatively, individual contributors who are expected to keep up with the latest technology can study specific ML techniques, like unsupervised learning.
Professionals who are interested in pivoting toward a career in machine learning might find short courses helpful as a tool to quickly acquire new knowledge in the field. As a career field, machine learning and data science is growing — employment of data scientists is projected to increase 36% from 2023 to 2033, much faster than the rate for all occupations.11
Enroll in machine learning courses, with certificate options, on GetSmarter and take your career to the next level.Sources
1(Dec, 2024). ‘Machine learning (ML) market size, share & COVID-19 impact analysis’. Retrieved from Fortune Business Insights.
2(n.d.). ‘What is machine learning?’. Retrieved from IBM.
3Brown, Sara. (Apr, 2021). ‘Machine learning, explained’. Retrieved from MIT Sloan.
4(Apr, 2024). ‘Difference between supervised and unsupervised learning’. Retrieved from GeeksforGeeks.
5(Sep, 2024). ‘Reinforcement learning’. Retrieved from GeeksforGeeks.
6Yasar, Kinza. (Jun, 2024). ‘Machine learning engineer (ML engineer)’. Retrieved from TechTarget.
7(Aug, 2024). ‘What data scientists do’. Retrieved from U.S. Bureau of Labor Statistics.
8Rouse, Margaret. (Nov, 2022). ‘What does data analyst mean?’. Retrieved from Techopedia.
9Belcic, Ivan. (May, 2024). ‘What is data engineering?’. Retrieved from IBM.
10Crivelaro, Celso. (Dec 2024). ‘Database developer: What it is, what they do, & salary’. Retrieved from Revelo.
11(Aug, 2024). ‘Job outlook’. Retrieved from U.S. Bureau of Labor Statistics.
Register for machine learning online courses
The sheer range of machine learning’s practical applications make the field both rapidly expanding and largely unavoidable in business. Whether it’s the recommended TV shows on a streaming service, autonomous vehicles on the streets, or the fraud detection services used by your bank, machine learning (ML) is everywhere. The global machine learning market is projected to grow from $26 billion in 2023 to about $226 billion by 2030.1
With machine learning online courses, professionals can explore this developing field and gain technical skills quickly.
What is machine learning?
Machine learning is a subfield of artificial intelligence (AI) that uses algorithms and data exposure to teach computers to make predictions.2
Traditional programming teaches computers via detailed instructions, comparable to a baking recipe that lists the exact ingredients and process for someone to follow. In machine learning (ML), computers learn via experience — a model trains itself on data to find patterns. With more data, the program becomes more accurate at its task.3
There are three primary types of machine learning. Supervised machine learning uses labeled data to train a model to achieve a known, desired output. In unsupervised machine learning, unlabeled data is used to help the model discover patterns without explicit instructions.4 Reinforcement learning uses a repeating system of trial and error so the machine learns from past experiences.5
Machine learning methods like computer vision, automatic speech recognition, and natural language processing are applied to all kinds of data, from audio and image files to massive spreadsheets. In turn, ML can be applied to a variety of use cases.
Some real-world examples of ML include:
- Recommendation systems
- Autonomous vehicles
- Fraud detection
- Chatbots
- Voice assistants
Jobs in machine learning
Machine learning is a varied research field; some careers are perfect for people who want to execute big ideas and there are others that are focused on a specific facet of the ML lifecycle. Often, ML could be just one part of how a professional solves a technical problem, operationalizes their goals, or builds a product.
Some examples of job titles that often use machine learning knowledge include:
- Machine learning engineer: Researches, builds, and designs AI systems to automate predictive models6
- Data scientist: Uses analytical tools like programming and data visualization to extract meaningful insights from data. These professionals can also develop machine learning algorithms7
- Data analyst: Collects, processes, and analyzes data from various sources to help achieve business goals8
- Data engineer: Builds and maintains an enterprise’s data infrastructure. These professionals can automate data integration and manage the data pipeline9
- Database developer: Designs, builds, tests, and maintains an organization’s databases. These professionals use programming languages to modify data structures and keep data secure10
Why should I enroll in machine learning short courses?
When you study machine learning, you gain skills in a rapidly evolving technology that influences every industry. Experienced leaders can explore how AI can work for their organization and study practical applications of machine learning in business. Alternatively, individual contributors who are expected to keep up with the latest technology can study specific ML techniques, like unsupervised learning.
Professionals who are interested in pivoting toward a career in machine learning might find short courses helpful as a tool to quickly acquire new knowledge in the field. As a career field, machine learning and data science is growing — employment of data scientists is projected to increase 36% from 2023 to 2033, much faster than the rate for all occupations.11
Enroll in machine learning courses, with certificate options, on GetSmarter and take your career to the next level.Sources
1(Dec, 2024). ‘Machine learning (ML) market size, share & COVID-19 impact analysis’. Retrieved from Fortune Business Insights.
2(n.d.). ‘What is machine learning?’. Retrieved from IBM.
3Brown, Sara. (Apr, 2021). ‘Machine learning, explained’. Retrieved from MIT Sloan.
4(Apr, 2024). ‘Difference between supervised and unsupervised learning’. Retrieved from GeeksforGeeks.
5(Sep, 2024). ‘Reinforcement learning’. Retrieved from GeeksforGeeks.
6Yasar, Kinza. (Jun, 2024). ‘Machine learning engineer (ML engineer)’. Retrieved from TechTarget.
7(Aug, 2024). ‘What data scientists do’. Retrieved from U.S. Bureau of Labor Statistics.
8Rouse, Margaret. (Nov, 2022). ‘What does data analyst mean?’. Retrieved from Techopedia.
9Belcic, Ivan. (May, 2024). ‘What is data engineering?’. Retrieved from IBM.
10Crivelaro, Celso. (Dec 2024). ‘Database developer: What it is, what they do, & salary’. Retrieved from Revelo.
11(Aug, 2024). ‘Job outlook’. Retrieved from U.S. Bureau of Labor Statistics.
