An introductory machine learning course taught by Andrew Ng, covering supervised and unsupervised learning, model fitting, and the practical process behind them. It is offered on Coursera and as Stanford lecture videos (CS 229 on YouTube).

Commenters call it a solid place to start for understanding what is happening in machine learning, and one suggests the Stanford CS 229 lectures as a deeper alternative to the lighter Coursera version.

On Reddit (3)

1. Strongly recommendscageousnicholsr/learnmachinelearning2019in “Math-Heavy MOOC for Machine Learning”▲ 1

   I would recommend watching the Stanford machine learning lectures on YouTube taught by Andrew Ng.

2. Recommendstralaleir/learnmachinelearning2020in “Where can I find additional, deeper math for Andrew Ng's formulas from his Coursera ML course?”▲ 3

   Stanford and Harvard courses do this no idea why but it's the norm I finished the course in matlab , It would've been better if it were on python but ngl It's a solid place to start and understand the process of what's happening and later on you could learn python from kaggle or The Data Science…

3. Recommendssudoankitr/productivity2020in “Is skillshare, brilliant, coursera and udemy worth it?”▲ 2

   I suggest you taking CS 229 on YouTube. Coursera's ML course is a toned down version of it.

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