Book Image

Machine Learning Quick Reference

By : Rahul Kumar
Book Image

Machine Learning Quick Reference

By: Rahul Kumar

Overview of this book

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.

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Table of Contents (18 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
About Packt
About Packt
Contributors
Contributors
Preface
Preface
Free Chapter
1
Quantifying Learning Algorithms
Quantifying Learning Algorithms
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
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
Performance in Ensemble Learning
What is ensemble learning?
Bagging
Decision tree
Random forest algorithm
Boosting
Summary
4
Training Neural Networks
Training Neural Networks
Neural networks
Network initialization
Overfitting
Prevention of overfitting in NNs
Vanishing gradient 
Recurrent neural networks
Summary
5
Time Series Analysis
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
Natural Language Processing
Text corpus
TF-IDF
Sentiment analysis
Topic modeling 
The Bayes theorem
Summary
7
Temporal and Sequential Pattern Discovery
Temporal and Sequential Pattern Discovery
Association rules
Apriori algorithm
Frequent pattern growth
Summary
8
Probabilistic Graphical Models
Probabilistic Graphical Models
Key concepts
Bayes rule
Bayes network
Summary
9
Selected Topics in Deep Learning
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
Causal Inference
Granger causality
F-test
Graphical causal models
Summary
11
Advanced Methods
Advanced Methods
Introduction
Kernel PCA
Independent component analysis
Compressed sensing
Self-organizing maps
Bayesian multiple imputation
Summary
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Index
Index

Statistical models

A statistical model is the approximation of the truth that has been captured through data and mathematics or statistics, and acts as an enabler here. This approximation is used to predict an event. A statistical model is nothing but a mathematical equation. 

For example, let's say we reach out to a bank for a home loan. What does the bank ask us? The first thing they would ask us to do is furnish lots of documents such as salary slips, identity proof documents, documents regarding the house we are going to purchase, a utility bill, the number of current loans we have, the number of dependants we have, and so on. All of these documents are nothing but the data that the bank would use to assess and check our creditworthiness:

What this means is that your creditworthiness is a function of the salary, number of loans, number of dependants, and so on. We can arrive at this equation or relationship mathematically.

Note

A statistical model is a mathematical equation that arrives at using given data for a particular business scenario.

In the next section, we will see how models learn and how the model can keep getting better.

Previous Section
End of Section 2
Next Section