Chapter
Statistical terminology for model building and validation
Major differences between statistical modeling and machine learning
Steps in machine learning model development and deployment
Statistical fundamentals and terminology for model building and validation
Bias versus variance trade-off
Machine learning terminology for model building and validation
Linear regression versus gradient descent
When to stop tuning machine learning models
Train, validation, and test data
Machine learning model overview
Chapter 2: Parallelism of Statistics and Machine Learning
Comparison between regression and machine learning models
Compensating factors in machine learning models
Assumptions of linear regression
Steps applied in linear regression modeling
Example of simple linear regression from first principles
Example of simple linear regression using the wine quality data
Example of multilinear regression - step-by-step methodology of model building
Backward and forward selection
Machine learning models - ridge and lasso regression
Example of ridge regression machine learning
Example of lasso regression machine learning model
Regularization parameters in linear regression and ridge/lasso regression
Chapter 3: Logistic Regression Versus Random Forest
Maximum likelihood estimation
Logistic regression – introduction and advantages
Terminology involved in logistic regression
Applying steps in logistic regression modeling
Example of logistic regression using German credit data
Example of random forest using German credit data
Grid search on random forest
Comparison of logistic regression with random forest
Chapter 4: 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
Tuning class weights in decision tree classifier
Random forest classifier - grid search
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
Chapter 5: K-Nearest Neighbors and Naive Bayes
Curse of dimensionality with 1D, 2D, and 3D example
KNN classifier with breast cancer Wisconsin data example
Tuning of k-value in KNN classifier
Understanding Bayes theorem with conditional probability
Naive Bayes classification
Naive Bayes SMS spam classification example
Chapter 6: Support Vector Machines and Neural Networks
Support vector machines working principles
Maximum margin classifier
Support vector classifier
SVM multilabel classifier with letter recognition data example
Maximum margin classifier - linear kernel
Artificial neural networks - ANN
Forward propagation and backpropagation
Optimization of neural networks
Stochastic gradient descent - SGD
Nesterov accelerated gradient - NAG
Adaptive moment estimation - Adam
Limited-memory broyden-fletcher-goldfarb-shanno - L-BFGS optimization algorithm
Dropout in neural networks
ANN classifier applied on handwritten digits using scikit-learn
Introduction to deep learning
Deep neural network classifier applied on handwritten digits using Keras
Chapter 7: Recommendation Engines
Advantages of collaborative filtering over content-based filtering
Matrix factorization using the alternating least squares algorithm for collaborative filtering
Evaluation of recommendation engine model
Hyperparameter selection in recommendation engines using grid search
Recommendation engine application on movie lens data
User-user similarity matrix
Movie-movie similarity matrix
Collaborative filtering using ALS
Grid search on collaborative filtering
Chapter 8: Unsupervised Learning
K-means working methodology from first principles
Optimal number of clusters and cluster evaluation
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
Model building technique using encoder-decoder architecture
Deep auto encoders applied on handwritten digits using Keras
Chapter 9: Reinforcement Learning
Introduction to reinforcement learning
Comparing supervised, unsupervised, and reinforcement learning in detail
Characteristics of reinforcement learning
Reinforcement learning basics
Category 2 - policy based
Category 3 - actor-critic
Fundamental categories in sequential decision making
Markov decision processes and Bellman equations
Algorithms to compute optimal policy using dynamic programming
Grid world example using value and policy iteration algorithms with basic Python
Comparison between dynamic programming and Monte Carlo methods
Key advantages of MC over DP methods
The suitability of Monte Carlo prediction on grid-world problems
Modeling Blackjack example of Monte Carlo methods using Python
Temporal difference learning
Comparison between Monte Carlo methods and temporal difference learning
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
Applications of reinforcement learning with integration of machine learning and deep learning
Automotive vehicle control - self-driving cars
Google DeepMind's AlphaGo
Statistics for Machine Learning
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