Practical Machine Learning Cookbook

Practical Machine Learning Cookbook

By : Atul Tripathi

Buy this Book

Practical Machine Learning Cookbook

By: Atul Tripathi

Buy this Book

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.

Practical Machine Learning Cookbook

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

Free Chapter

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

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

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

Model Selection and Regularization

Introduction

Shrinkage methods - calories burned per day

Dimension reduction methods - Delta's Aircraft Fleet

Principal component analysis - understanding world cuisine

Nonlinearity

Generalized additive models - measuring the household income of New Zealand

Smoothing splines - understanding cars and speed

Local regression - understanding drought warnings and impact

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

Unsupervised Learning

Introduction

Self-organizing map - visualizing of heatmaps

Vector quantization - image clustering

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

Structured Prediction

Introduction

Hidden Markov models - EUR and USD

Hidden Markov models - regime detection

Neural Networks

Introduction

Modelling SP 500

Measuring the unemployment rate

Deep Learning

Introduction

Recurrent neural networks - predicting periodic signals

Case Study - Exploring World Bank Data

Introduction

Exploring World Bank data

Case Study - Pricing Reinsurance Contracts

Introduction

Pricing reinsurance contracts

Case Study - Forecast of Electricity Consumption

Introduction

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Markov chains - the stocks regime switching model

In the last few decades, a lot of studies have been conducted on the analysis and forecasting of volatility. Volatility is the degree of variation of a trading price series over time as measured by the standard deviation of returns. Models of stock returns assume that the returns follow a geometric Brownian motion. This implies that over any discrete time interval, the return on stocks is log normally distributed and that returns in non-overlapping intervals are independent. Studies have found that this model fails to capture extreme price movements and stochastic variability in the volatility parameter. Stochastic volatility takes discrete values, switching between these values randomly. This is the basis of the regime-switching lognormal process (RSLN).

Getting ready

In order to perform the Markov chains regime switching model we shall be using data collected from the Stock's dataset.

Step 1 - collecting and describing the data

The dataset...

Practical Machine Learning Cookbook

By : Atul Tripathi

Practical Machine Learning Cookbook

By: Atul Tripathi

Overview of this book

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Markov chains - the stocks regime switching model

Getting ready

Step 1 - collecting and describing the data