Too much focus on implementation details and not enough emphasis on state of the art theory. A new practitioner should start with probabilistic learning for the

Too much focus on implementation details and not enough emphasis on state of the art theory. A new practitioner should start with probabilistic learning for theory and deep learning on the practical side. A cursory overview of linear regression, decision trees, and the various linear algebra vector learning tricks is more than enough. They are mathematically interesting but not particularly useful except on sorted and cleaned tabular data. The best introduction to ML is by demonstrating its solutions to previously intractable classic problems: realistic text to speech, speech recognition, image classification, image generation, natural language processing. A boosted decision tree is rather limited in its applications (except for Kaggle competitions) that it should be something to learn after the fact. Understanding maximum likelihood and probability chaining at the information theoretic level is a lot more important than focusing on the nitty-gritty details of the Generalized Linear Model du jour. A good understanding of ideas like KL-divergence and messaging passing is more crucial than knowing which tree or forest algorithm to use. Many tutorials like to focus on linear and logistic regression because they are well understood and mathematically straightforward (for the educator). But machine learning is not purely a field of math, nor is it merely statistics. Too many practitioners never go beyond formula chugging and end up missing the forest for the trees. Sure a big enough model will solve almost any problem. However, without understanding the information theory and probabilistic explanations behind why a model can learn and function, ML will always remain a bag of tricks.