I have recommended this course in one form or another for years, and the 2022 rebuild made it easier to recommend, not harder. The original taught machine learning in Octave, which was always the one thing people grumbled about, and the new version moves everything into Python with NumPy and scikit-learn, which is what people actually use. The three courses take you from linear and logistic regression through neural networks and on to unsupervised learning, recommenders and reinforcement learning, and the sequencing is so well judged that each idea lands before the next one needs it. What still sets Andrew Ng apart is that he teaches the intuition first.
You do not just learn that gradient descent works, you come away with a mental picture of why it works, and that picture is what lets you debug a model later when something goes wrong. That is rare. Most courses hand you a recipe and move on. The trade-off is that the guided labs do a lot of the lifting for you.
You fill in the interesting lines, but a fair amount of scaffolding is already there, so it is entirely possible to finish a course feeling confident and then freeze the first time you open a blank notebook. My advice is to fight that by rebuilding at least one lab from an empty file, and by starting a small project of your own partway through. The other honest caveat is scope. This is a foundations specialization, not a bootcamp, and it will not by itself land you a job or fill a portfolio.
What it will do is give you the conceptual base that makes everything afterwards faster to learn, from the Deep Learning Specialization to fast.ai to just reading papers without drowning. For most people that base is exactly what is missing, and the fact that you can audit the whole thing for free and only pay if you want the certificate makes it one of the best-value entries in the entire field.