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Applied Machine Learning in Python: a Hands-on Guide with Code
Machine Learning Concepts
Workflow Construction and Coding
Probability Concepts
Loading and Plotting Data and Models
Univariate Analysis
Multivariate Analysis
Feature Transformations
Feature Ranking
Cluster Analysis
Density-based Clustering
Spectral Clustering
Principal Components Analysis
Multidimensional Scaling
Linear Regression
Ridge Regression
LASSO Regression
Bayesian Linear Regression
Naive Bayes
Polynomial Regression
k-Nearest Neighbours
Decision Trees
Bagging Tree and Random Forest
Gradient Boosting
Support Vector Machines
Time Series Analysis and Modeling
Conclusions
References
Repository
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Index