LearnDataSci’s 2024 guide to AI courses offers valuable insights that remain practical in 2025. After studying Brendan Martin’s recommendations, I want to share my thoughts on how these courses stack up and which might best suit your needs.
The guide highlights nine courses, ranging from basic introductions to specialized technical programs. What caught my attention was the practical organization: instead of just listing popular options, Martin considers different starting points based on your background.
If you’ve never worked with AI before, Andrew Ng’s “AI For Everyone” makes sense as a starting point. It skips the complex math and coding, focusing instead on helping you understand what AI can and can’t do. But the real strength of Martin’s guide shows in its recommendations for people already working in tech. Software developers might want to start with Harvard’s Computer Science for AI Certificate, while data scientists could jump straight into the Deep Learning Specialization.
I particularly liked the inclusion of ETH Zurich’s Self-Driving Cars course with its Duckietown kit. It’s refreshing to see a hands-on approach where you actually build and program a small robot rather than just watching videos about theory.
The guide also tackles the price question head-on. You don’t always have to pay for quality content – MIT and Stanford offer their courses free online. Sure, you miss out on certificates and direct teacher feedback, but the material is exactly what their regular students learn. Meanwhile, programs like Udacity’s cost more but include mentoring and career support.
Martin’s assessment of what to avoid proves equally useful. He points out that IBM’s courses, despite high ratings on Coursera, often disappoint with their corporate slideshow approach and focus on selling their own cloud platform. He also warns about DeepLearning.ai’s AWS course being too basic for developers yet too technical for business users.
What matters most isn’t completing a specific course but building practical knowledge you can use. The best approach often combines different resources – maybe start with a structured course for foundations, then practice with real projects, and keep learning through documentation and community discussions.
These courses won’t make anyone an instant AI expert, but they provide solid starting points for different learning paths. Whether you’re looking to understand AI basics or build advanced applications, Martin’s guide helps cut through the noise and find courses worth your time.
Based on LearnDataSci’s AI course guide by Brendan Martin. Original at LearnDataSci.com.