Table of Contents

1. Preface
   1. Who this book is for
   2. What this book covers
   3. To get the most out of this book
      1. Download the example code files
      2. Download the color images
      3. Conventions used
   4. Get in touch
      1. Reviews
1. Quantifying Learning Algorithms
   1. Statistical models
   2. Learning curve
      1. Machine learning
      2. Wright's model
   3. Curve fitting
      1. Residual
   4. Statistical modeling – the two cultures of Leo Breiman
   5. Training data development data – test data
      1. Size of the training, development, and test set
   6. Bias-variance trade off
   7. Regularization
      1. Ridge regression (L2)
      2. Least absolute shrinkage and selection operator 
   8. Cross-validation and model selection
      1. K-fold cross-validation
   9. Model selection using cross-validation
   10. 0.632 rule in bootstrapping
   11. Model evaluation
       1. Confusion matrix
   12. Receiver operating characteristic curve
       1. Area under ROC
   13. H-measure
   14. Dimensionality reduction
   15. Summary
2. Evaluating Kernel Learning
   1. Introduction to vectors
      1. Magnitude of the vector
      2. Dot product
   2. Linear separability
   3. Hyperplanes 
   4. SVM
      1. Support vector
   5. Kernel trick
      1. Kernel
      2. Back to Kernel trick
   6. Kernel types
      1. Linear kernel
      2. Polynomial kernel
      3. Gaussian kernel
   7. SVM example and parameter optimization through grid search
   8. Summary
3. Performance in Ensemble Learning
   1. What is ensemble learning?
      1. Ensemble methods 
         1. Bootstrapping
   2. Bagging
   3. Decision tree
      1. Tree splitting
      2. Parameters of tree splitting
   4. Random forest algorithm
      1. Case study
   5. Boosting
      1. Gradient boosting
         1. Parameters of gradient boosting
   6. Summary
4. Training Neural Networks
   1. Neural networks
      1. How a neural network works
      2. Model initialization
      3. Loss function
      4. Optimization
      5. Computation in neural networks
         1. Calculation of activation for H1
      6. Backward propagation
      7. Activation function
         1. Types of activation functions
   2. Network initialization
      1. Backpropagation
   3. Overfitting
   4. Prevention of overfitting in NNs
   5. Vanishing gradient 
      1. Overcoming vanishing gradient
   6. Recurrent neural networks
      1. Limitations of RNNs
      2. Use case
   7. Summary
5. Time Series Analysis
   1. Introduction to time series analysis
   2. White noise
      1. Detection of white noise in a series
   3. Random walk
   4. Autoregression
   5. Autocorrelation
   6. Stationarity
      1. Detection of stationarity
   7. AR model
   8. Moving average model
   9. Autoregressive integrated moving average
   10. Optimization of parameters
       1. AR model
       2. ARIMA model
   11. Anomaly detection
   12. Summary
6. Natural Language Processing
   1. Text corpus
      1. Sentences
      2. Words
         1. Bags of words
   2. TF-IDF
      1. Executing the count vectorizer
      2. Executing TF-IDF in Python
   3. Sentiment analysis
      1. Sentiment classification
         1. TF-IDF feature extraction
         2. Count vectorizer bag of words feature extraction
            1. Model building count vectorization
   4. Topic modeling 
      1. LDA architecture
      2. Evaluating the model
      3. Visualizing the LDA
      4. The Naive Bayes technique in text classification
   5. The Bayes theorem
      1. How the Naive Bayes classifier works
   6. Summary
7. Temporal and Sequential Pattern Discovery
   1. Association rules
   2. Apriori algorithm
      1. Finding association rules
   3. Frequent pattern growth
      1. Frequent pattern tree growth
      2. Validation 
         1. Importing the library
   4. Summary
8. Probabilistic Graphical Models
   1. Key concepts
   2. Bayes rule
   3. Bayes network
      1. Probabilities of nodes
      2. CPT
      3. Example of the training and test set
   4. Summary
9. Selected Topics in Deep Learning
   1. Deep neural networks
      1. Why do we need a deep learning model?
      2. Deep neural network notation
      3. Forward propagation in a deep network
      4. Parameters W and b
      5. Forward and backward propagation
      6. Error computation
   2. Backward propagation
   3. Forward propagation equation
   4. Backward propagation equation
   5. Parameters and hyperparameters
   6. Bias initialization
      1. Hyperparameters
      2. Use case – digit recognizer
   7. Generative adversarial networks
   8. Hinton's Capsule network
      1. The Capsule Network and convolutional neural networks
   9. Summary
10. Causal Inference
    1. Granger causality
    2. F-test
       1. Limitations
       2. Use case
    3. Graphical causal models
    4. Summary
11. Advanced Methods
    1. Introduction
    2. Kernel PCA
    3. Independent component analysis
       1. Preprocessing for ICA
       2. Approach
    4. Compressed sensing
       1. Our goal
    5. Self-organizing maps
       1. SOM
    6. Bayesian multiple imputation
    7. Summary
12. Other Books You May Enjoy
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