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You're reading from Machine Learning Quick Reference Quick and essential machine learning hacks for training smart data models

Product type Paperback

Published in Jan 2019

Publisher Packt

ISBN-13 9781788830577

Length 294 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Kumar

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Table of Contents (18) Chapters

Title Page

About Packt

Contributors

Preface

1. Quantifying Learning Algorithms FREE CHAPTER

Statistical models
Learning curve
Curve fitting
Statistical modeling – the two cultures of Leo Breiman
Training data development data – test data
Bias-variance trade off
Regularization
Cross-validation and model selection
Model selection using cross-validation
0.632 rule in bootstrapping
Model evaluation
Receiver operating characteristic curve
H-measure
Dimensionality reduction
Summary

2. Evaluating Kernel Learning

Introduction to vectors
Linear separability
Hyperplanes
SVM
Kernel trick
Kernel types
SVM example and parameter optimization through grid search
Summary

3. Performance in Ensemble Learning

What is ensemble learning?
Bagging
Decision tree
Random forest algorithm
Boosting
Summary

4. Training Neural Networks

Neural networks
Network initialization
Overfitting
Prevention of overfitting in NNs
Vanishing gradient
Recurrent neural networks
Summary

5. Time Series Analysis

Introduction to time series analysis
White noise
Random walk
Autoregression
Autocorrelation
Stationarity
AR model
Moving average model
Autoregressive integrated moving average
Optimization of parameters
Anomaly detection
Summary

6. Natural Language Processing

Text corpus
TF-IDF
Sentiment analysis
Topic modeling
The Bayes theorem
Summary

7. Temporal and Sequential Pattern Discovery

Association rules
Apriori algorithm
Frequent pattern growth
Summary

8. Probabilistic Graphical Models

Key concepts
Bayes rule
Bayes network
Summary

9. Selected Topics in Deep Learning

Deep neural networks
Backward propagation
Forward propagation equation
Backward propagation equation
Parameters and hyperparameters
Bias initialization
Generative adversarial networks
Hinton's Capsule network
Summary

10. Causal Inference

Granger causality
F-test
Graphical causal models
Summary

11. Advanced Methods

Introduction
Kernel PCA
Independent component analysis
Compressed sensing
Self-organizing maps
Bayesian multiple imputation
Summary

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Table of Contents (18) Chapters

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Table of Contents (18) Chapters

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