Machine learning quick reference : quick and essential machine learning hacks for training smart data models / Rahul Kumar.

Format:

Book

Author/Creator:

Kumar, Rahul (Artificial intelligence scientist), author.

Language:

English

Subjects (All):

Machine learning.

Physical Description:

1 online resource (294 pages)

Edition:

1st edition

Place of Publication:

Birmingham : Packt, 2019.

System Details:

text file

Summary:

Your hands-on reference guide to developing, training, and optimizing your machine learning models Key Features Your guide to learning efficient machine learning processes from scratch Explore expert techniques and hacks for a variety of machine learning concepts Write effective code in R, Python, Scala, and Spark to solve all your machine learning problems Book Description 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. What you will learn Get a quick rundown of model selection, statistical modeling, and cross-validation Choose the best machine learning algorithm to solve your problem Explore kernel learning, neural networks, and time-series analysis Train deep learning models and optimize them for maximum performance Briefly cover Bayesian techniques and sentiment analysis in your NLP solution Implement probabilistic graphical models and causal inferences Measure and optimize the performance of your machine learning models Who this book is for If you're a machine learning practitioner, data scientist, machine learning developer, or engineer, this book will serve as a reference point in building machine learning solutions. You will also find this book useful if you're an intermediate machine learning developer or data scientist looking for a quick, handy reference to all the concepts of machine learning. You'll need some exposure to machine learning to get the best out of this book.

Contents:

Cover

Title Page

Copyright and Credits

About Packt

Contributors

Table of Contents

Preface

Chapter 1: 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

Chapter 2: 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

Chapter 3: 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

Chapter 4: 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

Chapter 5: 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

ARIMA model

Anomaly detection

Chapter 6: 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

Chapter 7: Temporal and Sequential Pattern Discovery

Association rules

Apriori algorithm

Finding association rules

Frequent pattern growth

Frequent pattern tree growth

Validation

Importing the library

Chapter 8: Probabilistic Graphical Models

Key concepts

Bayes rule

Bayes network

Probabilities of nodes

CPT

Example of the training and test set

Chapter 9: 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

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

Chapter 10: Causal Inference

Granger causality

F-test

Limitations

Graphical causal models

Chapter 11: Advanced Methods

Introduction

Kernel PCA

Independent component analysis

Preprocessing for ICA

Approach

Compressed sensing

Our goal

Self-organizing maps

SOM

Bayesian multiple imputation

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Index.

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed February 20, 2019).

OCLC:

1086042416