Practical Machine Learning Cookbook

Building Machine Learning applications with R

Practical Machine Learning Cookbook

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Atul Tripathi

Building Machine Learning applications with R
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Book Details

ISBN 139781785280511
Paperback570 pages

Book Description

Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations.

The first half of the book provides recipes on fairly complex machine-learning systems, where you’ll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more.

The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one.

Table of Contents

Chapter 1: Introduction to Machine Learning
What is machine learning?
An overview of classification
An overview of clustering
An overview of supervised learning
An overview of unsupervised learning
An overview of reinforcement learning
An overview of structured prediction
An overview of neural networks
An overview of deep learning
Chapter 2: Classification
Introduction
Discriminant function analysis - geological measurements on brines from wells
Multinomial logistic regression - understanding program choices made by students
Tobit regression - measuring the students' academic aptitude
Poisson regression - understanding species present in Galapagos Islands
Chapter 3: Clustering
Introduction
Hierarchical clustering - World Bank sample dataset
Hierarchical clustering - Amazon rainforest burned between 1999-2010
Hierarchical clustering - gene clustering
Binary clustering - math test
K-means clustering - European countries protein consumption
K-means clustering - foodstuff
Chapter 4: Model Selection and Regularization
Introduction
Shrinkage methods - calories burned per day
Dimension reduction methods - Delta's Aircraft Fleet
Principal component analysis - understanding world cuisine
Chapter 5: Nonlinearity
Generalized additive models - measuring the household income of New Zealand
Smoothing splines - understanding cars and speed
Local regression - understanding drought warnings and impact
Chapter 6: Supervised Learning
Introduction
Decision tree learning - Advance Health Directive for patients with chest pain
Decision tree learning - income-based distribution of real estate values
Decision tree learning - predicting the direction of stock movement
Naive Bayes - predicting the direction of stock movement
Random forest - currency trading strategy
Support vector machine - currency trading strategy
Stochastic gradient descent - adult income
Chapter 7: Unsupervised Learning
Introduction
Self-organizing map - visualizing of heatmaps
Vector quantization - image clustering
Chapter 8: Reinforcement Learning
Introduction
Markov chains - the stocks regime switching model
Markov chains - the multi-channel attribution model
Markov chains - the car rental agency service
Continuous Markov chains - vehicle service at a gas station
Monte Carlo simulations - calibrated Hull and White short-rates
Chapter 9: Structured Prediction
Introduction
Hidden Markov models - EUR and USD
Hidden Markov models - regime detection
Chapter 10: Neural Networks
Introduction
Modelling SP 500
Measuring the unemployment rate
Chapter 11: Deep Learning
Introduction
Recurrent neural networks - predicting periodic signals
Chapter 12: Case Study - Exploring World Bank Data
Introduction
Exploring World Bank data
Chapter 13: Case Study - Pricing Reinsurance Contracts
Introduction
Pricing reinsurance contracts
Chapter 14: Case Study - Forecast of Electricity Consumption
Introduction

What You Will Learn

  • Get equipped with a deeper understanding of how to apply machine-learning techniques
  • Implement each of the advanced machine-learning techniques
  • Solve real-life problems that are encountered in order to make your applications produce improved results
  • Gain hands-on experience in problem solving for your machine-learning systems
  • Understand the methods of collecting data, preparing data for usage, training the model, evaluating the model’s performance, and improving the model’s performance

Authors

Table of Contents

Chapter 1: Introduction to Machine Learning
What is machine learning?
An overview of classification
An overview of clustering
An overview of supervised learning
An overview of unsupervised learning
An overview of reinforcement learning
An overview of structured prediction
An overview of neural networks
An overview of deep learning
Chapter 2: Classification
Introduction
Discriminant function analysis - geological measurements on brines from wells
Multinomial logistic regression - understanding program choices made by students
Tobit regression - measuring the students' academic aptitude
Poisson regression - understanding species present in Galapagos Islands
Chapter 3: Clustering
Introduction
Hierarchical clustering - World Bank sample dataset
Hierarchical clustering - Amazon rainforest burned between 1999-2010
Hierarchical clustering - gene clustering
Binary clustering - math test
K-means clustering - European countries protein consumption
K-means clustering - foodstuff
Chapter 4: Model Selection and Regularization
Introduction
Shrinkage methods - calories burned per day
Dimension reduction methods - Delta's Aircraft Fleet
Principal component analysis - understanding world cuisine
Chapter 5: Nonlinearity
Generalized additive models - measuring the household income of New Zealand
Smoothing splines - understanding cars and speed
Local regression - understanding drought warnings and impact
Chapter 6: Supervised Learning
Introduction
Decision tree learning - Advance Health Directive for patients with chest pain
Decision tree learning - income-based distribution of real estate values
Decision tree learning - predicting the direction of stock movement
Naive Bayes - predicting the direction of stock movement
Random forest - currency trading strategy
Support vector machine - currency trading strategy
Stochastic gradient descent - adult income
Chapter 7: Unsupervised Learning
Introduction
Self-organizing map - visualizing of heatmaps
Vector quantization - image clustering
Chapter 8: Reinforcement Learning
Introduction
Markov chains - the stocks regime switching model
Markov chains - the multi-channel attribution model
Markov chains - the car rental agency service
Continuous Markov chains - vehicle service at a gas station
Monte Carlo simulations - calibrated Hull and White short-rates
Chapter 9: Structured Prediction
Introduction
Hidden Markov models - EUR and USD
Hidden Markov models - regime detection
Chapter 10: Neural Networks
Introduction
Modelling SP 500
Measuring the unemployment rate
Chapter 11: Deep Learning
Introduction
Recurrent neural networks - predicting periodic signals
Chapter 12: Case Study - Exploring World Bank Data
Introduction
Exploring World Bank data
Chapter 13: Case Study - Pricing Reinsurance Contracts
Introduction
Pricing reinsurance contracts
Chapter 14: Case Study - Forecast of Electricity Consumption
Introduction

Book Details

ISBN 139781785280511
Paperback570 pages
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