Machine Learning Mastery

Contrary to the other advice around here, I would strongly advise NOT taking a course. I think it is a good idea at some point, but it is not the first thing you should be doing.

The very first thing you should do is play! Identify a dataset you are interested in and get the entire machine learning pipeline up and running for it. Here's how I would go about it.

1) Get Jupyter up and running. You don't really need to do much to set it up. Just grab a Docker image.

2) Choose a dataset.

I wouldn't collect my own data first thing. I would just choose something that's already out there. You don't want to be bogged down by having to wrangle data into the format you need while learning NumPy and Pandas at the same time. You can find some interesting datasets here:

http://scikit-learn.org/stable/datasets/

And don't go with a neural net first thing, even though it is currently in vogue. It requires a lot of tuning before it actually works. Go with a gradient-boosted tree. It works well enough out of the box.

3) Write a classifier for it. Set up the entire supervised machine learning pipeline. Become familiar with feature extraction, feature importance, feature selection, dimensionality reduction, model selection, hyperparameter tuning using grid search, cross-validation, ....

For this step, let scikit-learn be your guide. It has terrific tutorials, and the documentation is a better educational resource than beginning coursework.

http://scikit-learn.org/stable/tutorial/

4)…

So, there's a lot of material out there but it's disjointed. If you get annoyed implementing your own neural net with only Python + Numpy, you might try some more complex examples just to immediately point at them and say "I ran this" and "It did that". (The article uses neural networks so I'm addressing that here even though your question and the article title use the much broader 'ML'.)

1) Brandon Rohrer, now Data Scientist at Facebook, has a few great talks, including one on Bayes' Theorem/Bayesian Inference - https://m.youtube.com/playlist?list=PLVZqlMpoM6kbaeySxhdtgQPFEC5nV7Faa

2) When asking future data scientists what tutorials for ML/NNs they like, they have usually found http://machinelearningmastery.com/ through Google and swear by it.

3) Josh Gordon, Developer Advocate at Google, has some simple ML/DL videos up in a 'Recipes' playlist: https://m.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal

If you want to just step through other people's code, you can do that too. Disclaimer: I put the below list together and it's not for ML broadly but for DL. That said if you want to run some examples fast and see the output, a number of folks have made that work for you -

I for one was floored to find great iOS examples (admittedly now deprecated for iOS 11). But If you have an iPhone with Metal (5s and up) Matthijs Hollemans - who wrote the iOS Apprentice at Ray Wenderlich - has Inception, YOLO, and MobileNets pre-trained and ready to go using Xcode, and…

I think one of the issues that makes tensors difficult to understand is that different disciplines use the word in different ways. I'm going to generalize here a bit about how each group uses the term, but I suspect not everyone will agree with me. Apologies in advance!

For machine-learning types, a tensor is a multidimensaional array of numbers that admits certain operations (addition, subtraction, hadamard product, etc) [0]

For mathematicians, a tensor is a function F. We pass the function an ordered tuple of N vectors and M covectors (or, to keep things simple, N column vectors and M row vectors), and the function returns a scalar. The function F needs to be linear in each of the N vectors and M covectors. In this view, a matrix is a tensor with N=1, M=1. The operations used by machine learning types arise naturally once you crank the mathematical handle a little bit.

For physicists, a tensor is what mathematicians would refer to as a tensor field. So they take a space X (think R^N), and for each point in the space they associate a mathematician's tensor F as defined above. The properties of the function F are permitted to change from point to point. The Riemannian metric tensor is a classic example of 'tensor' according to this usage.

[0] https://machinelearningmastery.com/introduction-to-tensors-for-machine-learning/

Some older books that are good reading:

https://www.amazon.com/Networks-Recognition-Advanced-Econometrics-Paperback/dp/0198538642

https://machinelearningmastery.com/neural-networks-tricks-of-the-trade-review/

There is something to say for downloading models from huggingface and just going from there. The fact is you are never going to train a foundation model but you can do useful tasks with one in minutes and if the one you use isn't good for your application try another one. See

https://sbert.net/

particularly the "usage" example that is right there. If you have 1000-10,000 short texts and put them through k-means clustering

https://scikit-learn.org/stable/modules/clustering.html#k-means

your jaw will drop or at least mine did because for years I have done clustering with bag of words, LDA and methods like that and when I applied to my RSS feed all the sports articles ended up in one place, the ukraine articles in another place, deep learning there, and reinforcement learning there... In like 30 minutes worth of work and faster than my LDA clustering engine. With DBSCAN clustering I get all the articles about the same news event clustered... It's just amazing.

Unlike the other subjects contained in this book, I do not think that any useful understanding of Probability and Statistics could result from only one or two chapters of coverage.

I would recommend a book I found that covers Statistics using Python:

Statistical Methods for Machine Learning
https://machinelearningmastery.com/products/

Personally, I much prefer that book to the free "Think Stats" one available.

You can also find a book there on Linear Algebra and others related to Deep Learning, all containing real implementations with Python.

I have no affiliation with the site; these books have proven so valuable to me that I need to share them.

Anyway, the book mentioned in the OP looks like a great foundation and I picked up a copy as a refresher. I would have also liked to have seen coverage of number theory in this book, with RSA being an application, but that too would have required lengthy explanation for any useful insights to be learned.

> How good will your deep fake have to be in order to fool a deep fake detection AI?

The best generator and the best detector are actually part of the same model! If you create a better detector, you are making the generator better at the same time so you have not accomplished anything.

"GANs are a clever way of training a generative model by framing the problem as a supervised learning problem with two sub-models: the generator model that we train to generate new examples, and the discriminator model that tries to classify examples as either real (from the domain) or fake (generated). The two models are trained together in a zero-sum game, adversarial, until the discriminator model is fooled about half the time, meaning the generator model is generating plausible examples."

https://machinelearningmastery.com/what-are-generative-adversarial-networks-gans/