Machine Learning 101 slidedeck: 2 years of headbanging, so you don't have to

I think slide 12 touches on this. Even in the case of an image we can process it pixel by pixel, but that would be lunacy!

For text great results have been achieved using automatons, but they only work for structured strings and break if you add only a little bit of noise.

I feel like ML should be considered whenever you feel like programming something requires you to deal with many different cases, you have a lot of example data available, and having some false positives / true negatives is not a big problem.

The document is awesome, but the animated backgrounds are distracting.

exactly, I stopped at the second slide because of that. "I ask for your undivided attention for two hours" is what it says, the background animation seems not helping that goal, quite the opposite.

Maybe. OTOH, this guy's resume says he's a web programmer. I'd think if google were recruiting people interested in machine learning, they'd get one of their machine learning specialists to write this.

As someone who works with a lot of people new to machine learning, I appreciate guides like this. I especially like the early slides that help frame AI vs ML vs DL so that people can have a realistic understanding of what these technologies are for.

For my part, one of the biggest realization I had after many years of applying machine learning was that I got too caught up in the machine learning algorithms themselves. I was often way too eager to guess and check across different algorithms and parameters in search of higher accuracy. Fortunately, there are new automated tools today that can do that automatically.

However, the key piece of advice I'd give someone new to machine learning is not to get caught up in the different machine learning techniques (SVM vs random forrest vs neural network, etc). Instead (1) spend more time on translating your problem into terms a machine can understand (i.e how are you defining and generating your labels) and (2) how do you perform feature engineering so the the right variables are available for machine learning to use. Focusing on these two things helped me build more accurate models that were more likely to be deployed in the real world.

Feature engineering in particular has become a bit of a passion of mine since that realization. I currently work on an open source project called Featuretools (https://github.com/featuretools/featuretools/) that aims help people apply feature engineering to transactional or relational datasets. We just put out a tutorial on building models to predict what product a customer will buy next, which is a good hands on example to learn from https://github.com/featuretools/predict_next_purchase for beginners.