Machine learning vs. artificial intelligence
The difference between machine learning vs. AI is bigger than you might think. Explore how these technologies work and how you can master them.
By: Thomas Broderick, Edited by: Gabriela Pérez Jordán, Reviewed by: Jeff Le
Last updated: June 30, 2025
Artificial intelligence (AI) is a broad field within computer science focused on simulating human intelligence, whereas machine learning (ML) is a subset of artificial intelligence focused on enabling systems to learn from data.
Understanding how artificial intelligence works is becoming increasingly important in today's job market. Discover the differences between machine learning and AI to develop essential skills and stay adaptable in today's evolving job landscape.
Machine learning vs. AI at a glance
Machine learning (ML)
- Subfield of artificial intelligence
- Learns from data
- Requires less manual programming
- Improves automatically through patterns and experience
- Performs tasks like prediction, pattern recognition, and automation
Artificial intelligence (AI)
- Broad field within computer science
- Focuses on simulating human intelligence
- Operates based on predefined rules and logic
- Requires extensive programming
- Performs tasks that require reasoning, planning, or problem solving
What is AI?
AI is a branch of computer science that dates back to the 1950s. The first AI were primitive chatbots programmed to offer advice on a narrow range of topics. Behind the scenes, complex algorithms analyzed users' inputs and provided appropriate outputs.
Although promising when computer scientists first introduced it, AI development slowed significantly in the latter half of the 20th century due to its limited functionality. It was only when computer scientists in this field developed machine learning that AI seemed capable of achieving what researchers decades before had promised.
What is machine learning?
Machine learning is one of many exciting subfields within AI. Machine learning researchers aim to build software that can learn from experience like a human child. The more information a machine learning program can absorb, the more it can mimic human behavior in a useful way.
Machine learning offers many advantages, such as unsupervised learning. Programmers need not develop the complex algorithms traditional AI software requires. The ultimate goal of machine learning research is artificial general intelligence (AGI), a computer system that can use its human-like intelligence in different tasks. However, AGI has not been achieved yet.
Key differences between AI and ML
- Machine learning is a branch of AI, and AI is a branch of computer science.
- Traditional AI relies on complex algorithms to process inputs and find information, while machine learning understands and retrieves information like a person.
- Traditional AI uses programmed responses, while machine learning predicts what users need by analyzing large datasets and language models.
- Advances in natural language processing make machine learning more user-friendly than traditional AI text chatbots.
- Machine learning can create new information (e.g., images, text) based on what it learns, while traditional AI cannot.
- Both fields require a foundation in mathematics, programming, and data analysis.
Explore AI and ML courses on edX
Should you learn AI or ML?
Both AI and ML offer exciting career paths, but the best choice for you depends on your interests and goals.
Choose AI...
If you are interested in creating intelligent systems with broad applications.
Choose ML...
If you want to specialize in data analysis, predictive modeling, and automation.
Consider both...
If you aspire to become a well-rounded tech expert ready for diverse opportunities.
Benefits of learning AI and machine learning
Why learn AI?
Building your knowledge and understanding of how to utilize AI can allow you to:
- Automate repetitive tasks
- Make better decisions
- Personalize user experiences
- Drive industry innovation
- Stay competitive
Why learn machine learning?
Improving your understanding of ML can unlock significant career and innovation opportunities, including the ability to:
- Extract insights
- Automate processes
- Learn predictive modeling
- Solve problems
- Advance your career
Real-world applications of AI and ML
Understand the difference between machine learning and AI with these industry-based examples.
How to start learning AI and machine learning
Dive into edX's AI educational resources and start your journey today.
- Take a machine learning or AI course with edX.
- Obtain an AI certificate to boost your résumé.
- Complete an AI bachelor's program if you are looking to qualify for entry-level positions or obtain an online master's in AI to advance your career.
- Become a more effective leader by completing an AI Executive Education program on edX.