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