更新时间：2021-06-10 18:34:10
coverpage
Title Page
Contributors
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Preface
Who this book is for
What this book covers
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Conventions used
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Reviews
Journey from Statistics to Machine Learning
Statistical terminology for model building and validation
Machine learning
Statistical fundamentals and terminology for model building and validation
Bias versus variance trade-off
Train and test data
Summary
Tree-Based Machine Learning Models
Introducing decision tree classifiers
Terminology used in decision trees
Decision tree working methodology from first principles
Comparison between logistic regression and decision trees
Comparison of error components across various styles of models
Remedial actions to push the model towards the ideal region
HR attrition data example
Decision tree classifier
Tuning class weights in decision tree classifier
Bagging classifier
Random forest classifier
Random forest classifier - grid search
AdaBoost classifier
Gradient boosting classifier
Comparison between AdaBoosting versus gradient boosting
Extreme gradient boosting - XGBoost classifier
Ensemble of ensembles - model stacking
Ensemble of ensembles with different types of classifiers
Ensemble of ensembles with bootstrap samples using a single type of classifier
K-Nearest Neighbors and Naive Bayes
K-nearest neighbors
KNN voter example
Curse of dimensionality
Curse of dimensionality with 1D 2D and 3D example
KNN classifier with breast cancer Wisconsin data example
Tuning of k-value in KNN classifier
Naive Bayes
Probability fundamentals
Joint probability
Understanding Bayes theorem with conditional probability
Naive Bayes classification
Laplace estimator
Naive Bayes SMS spam classification example
Unsupervised Learning
K-means clustering
K-means working methodology from first principles
Optimal number of clusters and cluster evaluation
The elbow method
K-means clustering with the iris data example
Principal Component Analysis - PCA
PCA working methodology from first principles
PCA applied on handwritten digits using scikit-learn
Singular value decomposition - SVD
SVD applied on handwritten digits using scikit-learn
Deep auto encoders
Model building technique using encoder-decoder architecture
Deep auto encoders applied on handwritten digits using Keras
Reinforcement Learning
Reinforcement learning basics
Category 1 - value based
Category 2 - policy based
Category 3 - actor-critic
Category 4 - model-free
Category 5 - model-based
Fundamental categories in sequential decision making
Markov decision processes and Bellman equations
Dynamic programming
Algorithms to compute optimal policy using dynamic programming
Grid world example using value and policy iteration algorithms with basic Python
Monte Carlo methods
Monte Carlo prediction
The suitability of Monte Carlo prediction on grid-world problems
Modeling Blackjack example of Monte Carlo methods using Python
Temporal difference learning
TD prediction
Driving office example for TD learning
SARSA on-policy TD control
Q-learning - off-policy TD control
Cliff walking example of on-policy and off-policy of TD control
Further reading
Hello Plotting World!
