Machine learning with Python cookbook: practical solutions from preprocessing to deep learning

Description

Vectors, matrices, and arrays -- Loading data -- Data wrangling -- Handling numerical data -- Handling categorical data -- Handling text -- Handling dates and times -- Handling images -- Dimensionalit reduction using feature extraction -- Dimensionality reduction using feature selection -- Model evaluation -- Model selection -- Linear regression -- Trees and forests -- K-nearest neighbors -- Logistic regression -- Support vector machines -- Naive bayes -- Clustering -- Neural networks -- Saving and loading trained models. Vectors, matrices, and arrays -- Loading data -- Data wrangling -- Handling numerical data -- Handling categorical data -- Handling text -- Handling dates and times -- Handling images -- Dimensionalit reduction using feature extraction -- Dimensionality reduction using feature selection -- Model evaluation -- Model selection -- Linear regression -- Trees and forests -- K-nearest neighbors -- Logistic regression -- Support vector machines -- Naive bayes -- Clustering -- Neural networks -- Saving and loading trained models.