Are people still learning Machine Learning or Deep Learning? Or am I already outdated?
AI and agents are talked about everywhere. "AI experts" are springing up overnight. Almost every coach, consultancy, or startup all has AI in their portfolio.
It's as if you're not part of the conversation unless you talk about AI.
And yes — it is the best time to explore bold business ideas with AI.
But I can't help noticing something: many people are skipping the fundamentals and jumping straight into building.
I'm the kind of person who needs to understand the foundation before I feel comfortable using it.
Seven years ago, I completed Andrew Ng's Machine Learning and Deep Learning courses.
My first job was in text mining and NLP, back when language models struggled to even grasp relationships and meaning in text.
That foundation shaped how I view the progress of AI today.
Because while tools and frameworks evolve fast, the principles remain the same.
Most companies I see still need solid data engineers and cloud experts before they are ready to deploy AI models.
So here's my advice to those passionate about AI:
Start by understanding the fundamentals. Learn how models work, how data trains, and why infrastructure matters.
And if you want to learn from the best — Stanford just released their CS230 (Autumn 2025) course for free, starting with Introduction to Deep Learning by Andrew Ng. Here is the link to the free course: https://www.youtube.com/watch?v=_NLHFoVNlbg
I'm curious —
👉 Who's still taking Andrew Ng's ML or DL courses in 2025?
Let me know in the comments — I'd love to hear your learning stories!
This is part of my "100 Days as Head of Data and Cloud" series. Follow along as I share insights, challenges, and learnings from this new leadership role.
#100Days #AIFirst #ContinuousLearning #MachineLearning #DataEngineering