Those courses include: “What is Data Science?,” “Tools for Data Science,” “Data Science Methodology,” “Data Analysis with Python,” and “Data Analysis with Python,” among others. You’ll get a total of 12 courses, with most clocking in at over ten hours each.
This series isn’t limited just to artificial intelligence, but it definitely devotes plenty of time to the concept. A few AI-specific courses include “Machine Learning with Python,” and one explicitly about upskilling your existing data scientist vocation, called “Generative AI: Elevate Your Data Science Career.”
Granted, it’s a big time and energy investment. But why take a piecemeal approach to learning? If it’s a natural fit for your interests, IBM’s course might be able to take your career in a whole new direction.
Sign up for the whole series — or any individual course you’re interested in — here on Coursera.
UW: Machine Learning Specialization
⏰Length: 80 hours
The University of Washington’s course on machine learning can take you from the basics to mastering the core machine learning fundamentals. It’ll cover topics including Regression, Classification, and Clustering and Retrieval, as well as the case studies you’ll need to get some practical understanding of the concepts.
According to the course itself, you will “learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.” As with the IBM courses, the programming language of choice is the general-purpose option Python.
You’ll need to have a little experience with computer programming to start this one, but it’s a great way to buckle down and start getting familiar with machine learning, the term for computer systems that adapt themselves by learning from data. At a pace of about ten hours a week, it should take you two months to complete.
Check it out here on Coursera.
