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Machine Learning Quick Reference

By : Kumar

Machine Learning Quick Reference

By: 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.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

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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

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

Performance in Ensemble Learning

Performance in Ensemble Learning

What is ensemble learning?

Bagging

Decision tree

Random forest algorithm

Boosting

Summary

Training Neural Networks

Training Neural Networks

Neural networks

Network initialization

Overfitting

Prevention of overfitting in NNs

Vanishing gradient

Recurrent neural networks

Summary

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

Natural Language Processing

Natural Language Processing

Text corpus

TF-IDF

Sentiment analysis

Topic modeling

The Bayes theorem

Summary

Temporal and Sequential Pattern Discovery

Temporal and Sequential Pattern Discovery

Association rules

Apriori algorithm

Frequent pattern growth

Summary

Probabilistic Graphical Models

Probabilistic Graphical Models

Key concepts

Bayes rule

Bayes network

Summary

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

Causal Inference

Causal Inference

Granger causality

F-test

Graphical causal models

Summary

Advanced Methods

Advanced Methods

Introduction

Kernel PCA

Independent component analysis

Compressed sensing

Self-organizing maps

Bayesian multiple imputation

Summary

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Kernel types

We're going to explain the types of in this section.

Linear kernel

Let's say there are two vectors, x₁and x₂, so the linear kernel can be defined by the following:

K(x_1, x₂)= x_{1 .}x₂

Polynomial kernel

If there are two vectors, x₁ and x₂, the linear kernel can be defined by the following:

K(x_1, x₂)= (x_{1 .}x₂+ c)^d

Where:

c: Constant
d: Degree of polynomial:

def polynomial_kernel(x1, x2, degree, constant=0): 
    result = sum([x1[i] * x2[i] for i in range(len(x1))]) + constant 
    return...

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Tech Concepts

36

Programming languages

73

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