Hands-on approach for implementing supervised learning algorithms like decision tree, RF, SVM, and Neural Nets with Python
Cover the mathematics of supervised learning algorithms in a lucid manner
Discusses common challenges like overfitting, data imbalance, hyperparameter tuning, outlier treatment