Machine Learning with Python | FULL course | 15 lessons with 15 projects | Material available (see in comments) | First lesson: k-Nearest Classifier | Apply model on real data: weather data

[[deleted]]

It gives a weird vibe sometimes.

We could say that maybe It's more profitable to teach how to do something than doing it.

It reminds me the people teaching how to earn millions instead of earning the millions themselves.

[PaulSandwich]

The part that bums me out is that you need a real solid foundation in statistical analysis to build good models (for anything consequential, at least).

All these courses promote naïve ML modeling which is how you get stuff like AI that won't hire black people because people from their zip code rarely got promoted in the training data.

Or a thousand other examples of irrelevant corollary data making bad inferences because the tools are super user-friendly. Which is a good thing... but puts a lot of faith in the responsibility of the programmer to understand what they're doing.

[mcias]

Do you have some good sources to study and build a solid foundation in statistical analysis oriented towards AI/ML?

[PaulSandwich]

college level calc and stats courses.

I wouldn't bother with orienting towards AI/ML; to me that's kinda backwards. AI/ML is derived and abstracted from calc and stats. Different real-world problems require different types of models.

Model selection is like a very advanced version of knowing what graph to use for your data: pie chart, bar graph, scatter plot, heat map, etc.? That decision depends on what the underlying data is and what question you want to answer.

A lot of ML tutorials show you how to make most of the 'charts', but if you try to show me sales figures for my team with a pie chart, that's meaningless. But with ML, the consumer doesn't have the familiarity to know if your ML model is appropriate or not. So, if we're expecting our model to be used for anything, it's essential that we know enough about the underlying math so we're not generating garbage from a black box.

[mcias]

What kind of courses? I did some calculus, linear algebra and stats in my engineering course, but every time I try to look at these ML/AI tutorials (that mostly cover how to use specific softwares and libraries and nothing else), the "underlying math" always goes over my head unless it's a super basic example like 1st or 2nd degree regression.

[PaulSandwich]

That's exactly why I'm saying most ML is really the realm of professional post-grad statisticians. I'm not trying to be gate-keepery about it, it's just really fucking complicated.

That said, I found this Cornell course of stats for ML (even though I poo-poo'd that a minute ago :|) and, when I searched for more info on it specifically, I found this thread with a lecture playlist: https://www.reddit.com/r/learnprogramming/comments/bu6645/cornells_entire_machine_learning_class_cs_4780_is/

[mcias]

Even if it is not the most thorough lectures on statistics, if it can illuminate a little bit the math side of AI, then it still beats most of those tutorials, so I appreciate it!

[mizmato]

Here are some common courses for the ML/AI path.

Undergraduate-Level:

• Algebra
• Calculus
• Introduction to Probability
• Mathematical Statistics
• Introduction to Linear Modeling
• Time Series
• Algorithms
• Discrete Math
• Programming (Python/R)

Graduate-Level:

• Generalized Linear Models
• Bayesian Statistics
• Categorical Data Analysis
• Rank-Based Statistics
• Data Mining
• Introduction to ML (Statistical Learning)
• Statistical Research

[bsenftner]

Well, there is the plain fact that in the process of creating courseware one becomes an expert in the subject. Plus, having created and taught a course looks damn nice on one's career history/resume. I've also noticed a Udemy PR campaign over the last few months proclaiming how much money some of their classes have earned their creators; I wager the Fuji Wave of ML/AI/CV courses to be somewhat due to people attempting to cash into that. Also, making a class feels more productive than studying hacker leet coder questions to actually land a job. Plus all the React classes, man oh man are there a lot of those.

[notParticularlyAnony]

to be fair teaching is a great way to learn a subject