Andrey’s Reviews > Feature Engineering Made Easy > Status Update

Is this being explained in the abstract or does it provide specific examples done in Python or R?

It has rather shallow explanations of algorithms, and many examples in Python using scikit-learn.

ok. What are the core algo's covered? GBM, Random Forrest, and Neural Networks?

Any discussion in the book on different data transforms like natural log, box cox, etc?

It's mostly about preprocessing: imputation of missing values, encoding of categorical values, regularization, standardization, vectorizers of textual data, PCA, LDA etc. Also, there is a chapter on the feature selection module from scikit-learn. It has no discussions of natural log or Box Cox transformations. If you want a more in-depth and heavier on math book on feature engineering you can take a look at Feature Engineering for Machine Learning. It's much better at explaining algorithms (and describes log and box cox transformations).

thanks for sharing Andrey. I have slowly made my way through Intro to Statistical learning and Elements of Statistical learning, both excellent books for ML.