[UmbertoNoEco]

No. The ugly truth is that these courses will be useless to 99% of the people. Machine learning is dominated by big corporations with gigantic amounts of data and processing power. If you want to work in one of them or create competing ML companies you need pedigree (a PhD from a well know university), and those guys arent taking courses with fake credentials.

You could use ML in your job/company but then you dont need this course, you just use a ML product.

See this course as a hobby thing, or if you are in HS and want to start preparing for college, otherwise there are better uses of your time.

[Tenoke]

There's a lot of ML happening outside of big corporations, which you can confirm by just searching 'machine learning' on any job site. While it's true that often you can use ready-made ML solutions, you often will benefit from additional knowledge for improving or adjusting them for your company's specific problem and while interviewing you will often be asked the kind of questions those courses cover.

[woah]

You can get almost unlimited GPU time on Google Colab for $50 a month. I don't know why or how they pay for this, but it does bring "real research" into the reach of individuals.

[UmbertoNoEco]

You can get more processing power and true unlimited time with any semi-competent graphic card (probably costing less than 1 year of Colab Pro+). Pro+ is a scam, you are not told what kind of instances you will be running at, and you dont have any guaranteed continuous running time. And even if you were given full 24/7 access to a top of the line card that would be like 0.001% of the power used to train any big modern ML model.

Users complain all the time: https://www.reddit.com/r/GoogleColab/comments/sq0lia/colab_p...

[mupuff1234]

> You could use ML in your job/company but then you dont need this course, you just use a ML product.

ML product?

[benrow]

For example, Google Vision API can do some out-of-the-box classification on arbitrary images with no training needed. Covers super common cases such as explicit content detection and object detection.

There are more customisable products within Google where you can provide training examples and labels using a UI (AutoML I think it's called). The result is an endpoint you can use to do inference, based on the model created behind the scenes.

I just mention these examples because I've spent a little time researching them at top-level.

[queuebert]

Or maybe people want to understand what's going on under the hood of the ML products they use?

[asey]

Of course, though in fairness parent was answering grandparent's specific question (and accurately in my experience)

[sydthrowaway]

How about joining FAANG as SWE, and then internal transfer?

[lupire]

Like the other 10K employees at the company with the same idea?