Self-Studying Machine Learning? Remind yourself of these 6 things
Machine learning engineer Daniel Bourke shares his tips for self-studying machine learning.
Learning machine learning can be tough. It's the combination of several fields which you could spend years learning each on their own.
What Makes a Good Feature? - Machine Learning Recipes #3
Good features are informative, independent, and simple. Here are some concepts by using a histogram to visualize a feature from a toy dataset.
10 Books to Learn Machine Learning
Here you will get an ordered reading list of free books to help anyone learn machine learning efficiently!
Black Box Machine Learning
With the abundance of well-documented machine learning (ML) libraries, it's fairly straightforward for a programmer to "do" ML, without any understanding of how things are working. And we encourage this "black boxes" machine learning! (At least to start.) However, to make proper use of these ML libraries, one needs to be conversant in the basic vocabulary, concepts, and workflows that underlie ML. We'll introduce the standard ML problem types (classification and regression) and discuss prediction functions, feature extraction, learning algorithms, performance evaluation, cross-validation, sample bias, nonstationarity, overfitting, and hyperparameter tuning.