Explore the best online machine learning courses

Learn machine learning online at your own pace and build in-demand skills for data-driven roles. Enroll in an edX course from top universities and industry leaders and earn a certificate.

Key takeaways

  • Study machine learning through edX courses from Harvard, Georgia Tech, IBM, and other university and industry partners.
  • You can learn Python, algorithms, statistics, data engineering, and machine learning fundamentals through online courses that typically last 2-12 weeks.
  • Prepare for data and AI-related roles across sectors such as finance, healthcare, tech, retail, and manufacturing.

Popular online machine learning courses with certificates

CS50's Introduction to Artificial Intelligence with Python
Harvard University
Course
Machine Learning and AI with Python
Harvard University
Course
Machine Learning with Python: from Linear Models to Deep Learning.
Massachusetts Institute of Technology
Course
Data Science: Building Machine Learning Models
Harvard University
Course
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What is machine learning?

Machine learning (ML) is a branch of artificial intelligence (AI) that uses algorithms and data to make predictions, spot trends, and support decisions.

Instead of relying on traditional programming, where a person writes fixed rules for every scenario, a machine learning model is trained on data and examples. Over time, the model identifies patterns and uses them to improve its outputs.

You may already interact with machine learning every day when your audio streaming service recommends music or your bank flags an unusual charge. In retail, similar techniques can help fashion brands forecast product demand before the next season begins.

Why learn machine learning?

  • Work in a field with solid career demand. The Bureau of Labor Statistics (BLS) projects employment for ML engineers and data scientists to grow 34% from 2024 to 2034, with a 2024 median annual salary of $112,590.
  • Pair technical skills with domain knowledge. ML can be more valuable when you understand the field in which you are applying it, whether that means medicine, energy, marketing, fashion, logistics, or public policy.
  • Keep up with how work is changing. The World Economic Forum lists AI and big data among the fastest-growing skills for 2025-2030, along with technology literacy and lifelong learning.
  • Create new tools, products, or business ideas. With the right problem and enough practice, machine learning skills can help you prototype recommendation tools, forecasting models, or AI-powered services.

Browse online Machine Learning Certificates

Find new interests and advance your career opportunities

Stand out in your field

Use the knowledge and skills you have gained to drive impact at work and grow your career.

Learn at your own pace

On your computer, tablet or phone, online courses make learning flexible to fit your busy life.

Earn a valuable credential

Showcase your key skills and valuable knowledge.

Certificates

Computer Science for Artificial Intelligence
Harvard University
Professional Certificate
2 Courses
Python for Data Science and Deep Learning
Harvard University
Professional Certificate
3 Courses
Python for Data Science and Machine Learning
Harvard University
Professional Certificate
2 Courses
Tiny Machine Learning (TinyML)
Harvard University
Professional Certificate
3 Courses
Applied Tiny Machine Learning (TinyML) for Scale
Harvard University
Professional Certificate
3 Courses
Contact Center AI (CCAI) Platform
Google Cloud
Professional Certificate
3 Courses
Artificial Intelligence & Machine Learning in Healthcare
MGH Institute of Health Professions
MicroMasters
2 Courses
Machine Learning
IBM
Professional Certificate
6 Courses

What careers can you pursue with machine learning skills?

Machine learning is not limited to one job title. Some professionals use ML to analyze data and explain business problems, while others build models, test algorithms, or apply AI methods inside a specific field. Your path may depend on both your technical training and the domain knowledge you bring.

These are typical career paths for professionals with ML knowledge, with salary data from the BLS:

Machine learning engineer

ML engineers help build, train, test, and improve models used in forecasting tools, recommendation systems, and AI applications. Because the BLS does not track "machine learning engineer" as a separate occupation, the salary below reflects the related occupation of data scientist.

  • Required education: Bachelor's degree in computer science, engineering, statistics, data science, or a similar field. Research roles may require a master's degree or a PhD.
  • BLS Median annual salary (2025): $120,230

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How do you start learning machine learning?

Learning machine learning is a long-term process, not a one-course shortcut. It takes practice, discipline, and a willingness to keep learning as tools and techniques evolve.

Universities and industry leaders, including Harvard, Stanford, IBM, and Microsoft, teach edX's machine learning courses. These rigorous online courses and certificate programs can support building that foundation step by step, flexibly.

Consider this path to start learning ML:

1. Start with Python, data tools, and math foundations

Begin by learning Python, basic programming logic, and data analysis. You don't need to master everything before you start your ML journey, but you will need enough exposure to code, datasets, and maths to understand what your models are doing.

Featured courses

CS50's Introduction to Programming with Python
Harvard University
Course
Computing in Python I: Fundamentals and Procedural Programming
The Georgia Institute of Technology
Course
Introduction to Data Science with Python
Harvard University
Course

Explore machine learning programs

Frequently asked questions

Do I need Python to learn machine learning?

Yes, you will likely need to know Python to learn machine learning. Some introductory edX courses may explain ML concepts without prior coding experience, but Python is used for working with datasets, training models, and using common machine learning libraries.

If your goal is an AI career or data science role, start building your Python skills early.

How long does it take to learn machine learning?

The time it takes to learn machine learning depends on your background and career goals; this could mean months or years. On edX, individual courses typically take 2-6 weeks, while Professional Certificates can take 8-12 months.

Learners new to artificial intelligence, programming, statistics, or data science may need more time to build a foundation. They might benefit from Professional Certificate programs in these areas, such as Python for Data Science and Machine Learning from Harvard or Machine Learning from IBM.

How can I start to learn machine learning?

Start by gaining Python, data analysis, and basic statistics skills, then move on to an introductory machine learning course. This experience can help you determine which other courses, certificates, or degrees can help you achieve your career goals.

Is machine learning difficult to learn?

Machine learning can be difficult because it combines artificial intelligence, programming, statistics, maths, data science, and abstract problem-solving. You do not need to understand everything at once, but you should expect a long learning curve.

The BLS notes that data scientists typically need at least a bachelor's degree in a quantitative or computing field, but research roles might require a master's or a doctorate.

What are the four types of machine learning?

The four types of machine learning are supervised, unsupervised, semisupervised, and reinforcement learning. Supervised learning algorithms learn by pairing labeled inputs with labeled outputs. Unsupervised learning algorithms find patterns in data independent of any instructions or labeling. Semisupervised learning algorithms use a fraction of the labeled data provided to supervised learning algorithms. Reinforcement learning algorithms learn through trial and error.

Can machine learning be self-taught?

You can learn machine learning fundamentals on your own, especially through structured courses, practice datasets, portfolio-building projects, and continuous learning.

However, to qualify for an ML engineer or data science job, most employers prefer candidates with a bachelor's degree or a graduate degree. Certificates, certifications, and a robust portfolio may also demonstrate your knowledge and skills.