Machine learning may sound relatively old-fashioned in the age of AI, but it remains a valuable and oft-used skill. Machine learning is the use of algorithms in computer systems to “learn” from data, allowing those systems to take on autonomous tasks. Manufacturing, engineering, programming, data science and more can include machine learning.
The field is distinct from AI in its approach, methods and underlying structure, and it often makes headlines in physics and other science applications. To discover more about machine learning, you can take online courses from a variety of businesses or institutions.
For beginners, Google’s Introduction to Machine Learning is a clear-cut, low-commitment option. This course is the first entry in a longer sequence of Google “foundational courses” on machine learning. That makes it easy to explore as much or as little of the topic as you want.
This course is free.
This course can be completed in 20 minutes.
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There are no prerequisites for this course.
Harvard University has some of the brightest minds in education behind its online courses — contributing to our selection of “Data Science: Machine Learning.” This course is a section in Harvard’s larger online data science course. It’s appropriate for people with some professional experience in data science, placing machine learning in the context of existing, practical work. This course results in a project the learner can use or show to current or prospective employers — namely, a movie recommendation system showing mastery of predictive algorithms.
“Data Science: Machine Learning” can be “audited” for free. Paying $149 adds a certification of completion and unlimited access to the course materials.
This course is self-paced. It has enough content for about eight weeks of work if done at 2 to 4 hours per week.
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It is recommended to take the previous courses in Professional Certificate Program in Data Science before taking this course.
While this certification includes self-paced elements, it also offers live discussions with peers and educators. Participants will get feedback on their work. The course includes projects suitable for a resume or other real-world demonstrations. It covers the math involved in machine learning — including linear algebra and probability distributions — and computing aspects, including kernel machines and neural networks.
This certification costs $3,750.
This course can be completed in 3.5 months at 6-9 hours of study per week.
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Cornell University recommends that learners taking this course have a background in “math, including familiarity with Python, probability theory, statistics, multivariate calculus and linear algebra.” Completing some projects requires using the NumPy library and Jupyter Notebooks.
Andrew Ng is often referred to as one of the best instructors of artificial intelligence. An adjunct professor at Stanford University and co-founder of Coursera, he has built a brand on conveying complex information in a useful, actionable way for people who want to progress in their tech careers. The Machine Learning Specialization contains three separate courses and covers neural networks, deep reinforcement learning and more.
This course is accessible through a Coursera Plus subscription at $59 per month.
Coursera estimates this self-paced course will take 2 months at 10 hours per week.
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Coursera recommends that learners taking this course have a background in “Basic coding (for loops, functions, if/else statements) and high school-level math (arithmetic, algebra).”
IBM instructors teach this machine learning course, which comprises four smaller courses:
This specialization includes hands-on exercises in SQL, regression, classification and other tools and techniques useful in ML. By the end of the course, you will be able to design ML systems to glean insights from data sets that lack a target or labeled variable. Upon completing the specialization, learners will earn a career certificate from IBM.
This specialization is accessible through a Coursera Plus subscription at $59 per month.
This specialization takes two months at 10 hours per week to complete.
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Learners pursuing this specialization should have some experience in coding, particularly in Python, as well as be comfortable with calculus, linear algebra, probability and statistics.
In choosing these courses, we looked at universities and online learning platforms well-known in the tech world. We sought to provide a mix of beginner, intermediate and advanced courses and certifications.
