Book Image

Practical Machine Learning Cookbook

By : Atul Tripathi
Book Image

Practical Machine Learning Cookbook

By: Atul Tripathi

Overview of this book

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.

Related Content you might be interested in

Current Title:

Small book image
Practical Machine Learning Cookbook
Atul Tripathi
Apr, 2017 | 570 pages
Small book image
Regression Analysis with R
Giuseppe Ciaburro
Jan, 2018 | 422 pages
Small book image
Machine Learning with R Cookbook
YuWei Chiu David Chiu | AshishSingh Bhatia
Oct, 2017 | 572 pages
Small book image
Neural Networks with R
Balaji Venkateswaran | Giuseppe Ciaburro
Sep, 2017 | 270 pages
Table of Contents (21 chapters)
Practical Machine Learning Cookbook
Practical Machine Learning Cookbook
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Introduction to Machine Learning
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
2
Classification
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
3
Clustering
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
4
Model Selection and Regularization
Model Selection and Regularization
Introduction
Shrinkage methods - calories burned per day
Dimension reduction methods - Delta's Aircraft Fleet
Principal component analysis - understanding world cuisine
5
Nonlinearity
Nonlinearity
Generalized additive models - measuring the household income of New Zealand
Smoothing splines - understanding cars and speed
Local regression - understanding drought warnings and impact
6
Supervised Learning
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
7
Unsupervised Learning
Unsupervised Learning
Introduction
Self-organizing map - visualizing of heatmaps
Vector quantization - image clustering
8
Reinforcement Learning
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
9
Structured Prediction
Structured Prediction
Introduction
Hidden Markov models - EUR and USD
Hidden Markov models - regime detection
10
Neural Networks
Neural Networks
Introduction
Modelling SP 500
Measuring the unemployment rate
11
Deep Learning
Deep Learning
Introduction
Recurrent neural networks - predicting periodic signals
12
Case Study - Exploring World Bank Data
Case Study - Exploring World Bank Data
Introduction
Exploring World Bank data
13
Case Study - Pricing Reinsurance Contracts
Case Study - Pricing Reinsurance Contracts
Introduction
Pricing reinsurance contracts
14
Case Study - Forecast of Electricity Consumption
Case Study - Forecast of Electricity Consumption
Introduction

Discriminant function analysis - geological measurements on brines from wells

Let us assume that a study of ancient artifacts that have been collected from mines needs to be carried out. Rock samples have been collected from the mines. On the collected rock samples geochemical measurements have been carried out. A similar study has been carried out on the collected artifacts. In order to separate the samples into the mine from which they were excavated, DFA can be used as a function. The function can then be applied to the artifacts to predict which mine was the source of each artifact.

Getting ready

In order to perform discriminant function analysis we shall be using a dataset collected from mines.

Step 1 - collecting and describing data

The dataset on data analysis in geology titled BRINE shall be used. This can be obtained from http://www.kgs.ku.edu/Mathgeo/Books/Stat/ASCII/BRINE.TXT . The dataset is in a standard form, with rows corresponding to samples and columns corresponding to variables...

Previous Section
End of Section 3
Next Section