Machine learning may seem relatively old-fashioned in the age of AI, but it remains a valuable and widely 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 all involve machine learning.
This field differs from AI in its approach, methods, and underlying structure, and it often makes headlines in physics and other scientific applications. To discover more about machine learning, you can take online courses from a variety of companies or institutions.
Best Machine Learning Courses: Comparison Table
Introduction to Machine Learning (Google) – Ideal for Absolute Beginners
For beginners, Google's Introduction to Machine Learning is a clear, low-commitment option. This course is the first entry in a longer sequence of Google's “foundation courses” on machine learning. That makes it easy to explore as much or as little of the topic as you want.
Prices
This course is free.
Duration
This course can be completed in 20 minutes.
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Prerequisites
There are no prerequisites for this course.
Data Science – Machine Learning (Harvard on edX): Best for Data Scientists
Harvard University has some of the brightest minds in education behind its online courses, which contributes to our selection of “Data Science: Machine Learning.” This course is a section of Harvard’s broader online data science course. It is appropriate for people with some professional experience in data science, as it places machine learning in the context of existing practical work. This course results in a project that the student can use or show to current or potential employers—i.e., a movie recommendation system that demonstrates mastery of predictive algorithms.
Prices
“Data Science: Machine Learning” is free to audit. Paying $149 gets you a certificate of completion and unlimited access to the course materials.
Duration
This course is self-paced and has enough content for about eight weeks of work if you complete 2-4 hours per week.
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Prerequisites
It is recommended to take the previous courses of the Professional Certification Program in Data Science before taking this course.
Machine Learning Certificate Program from Cornell University (Cornell) – Ideal for a Traditional College Education
While this certification includes self-paced elements, it also offers live discussions with peers and educators. Participants will receive feedback on their work. The course includes projects suitable for a resume or other real-world demonstrations. It covers the mathematics involved in machine learning (including linear algebra and probability distributions) and computer science aspects, including kernel machines and neural networks.
Prices
This certification costs $3,750.
Duration
This course can be completed in 3.5 months with 6-9 hours of study per week.
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Prerequisites
Cornell University recommends that students taking this course have a background in “mathematics, including familiarity with Python, probability theory, statistics, multivariate calculus, and linear algebra.” Some projects require the use of the NumPy library and Jupyter Notebooks.
Stanford Machine Learning Specialization (Coursera) – Ideal for building neural network applications
Andrew Ng is often considered one of the best AI instructors out there. An adjunct professor at Stanford University and co-founder of Coursera, he has built a brand that conveys complex information in a useful and practical way for people who want to advance their tech careers. The machine learning specialization contains three standalone courses and covers neural networks, deep reinforcement learning, and more.
Prices
This course is accessible through a Coursera Plus subscription at $59 per month.
Duration
Coursera estimates this self-paced course will take 2 months, 10 hours per week.
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Prerequisites
Coursera recommends that students taking this course have knowledge of “basic coding (for loops, functions, if/else statements) and high school level math (arithmetic, algebra).”
IBM Introduction to Machine Learning Specialization (Coursera) – Ideal for aspiring data scientists
IBM instructors teach this machine learning course, which consists of four smaller courses:
- Exploratory data analysis for machine learning.
- Supervised machine learning: regression.
- Supervised machine learning: classification.
- Unsupervised machine learning.
This specialization includes hands-on exercises in SQL, regression, classification, and other useful tools and techniques in ML. Upon completion of the course, students will be able to design ML systems to gain insights from data sets that lack a target or labeled variable. Upon completion of the specialization, students will earn an IBM Professional Certificate.
Prices
This specialization is accessible through a Coursera Plus subscription at $59 per month.
Duration
This specialization lasts two months, 10 hours per week.
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Prerequisites
Students pursuing this specialization should have some coding experience, particularly in Python, as well as be comfortable with calculus, linear algebra, probability, and statistics.
Methodology
When choosing these courses, we analyzed universities and online learning platforms recognized in the world of technology. We sought to offer a combination of courses and certifications for beginners, intermediates, and advanced learners.
