Packt+ | Advance your knowledge in tech

0

Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Free Learning

Practical Machine Learning Cookbook

You're reading from Practical Machine Learning Cookbook Supervised and unsupervised machine learning simplified

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781785280511

Length 570 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Machine Learning

Author (1):

Atul Tripathi

View More author details

Table of Contents (15) Chapters

Preface

1. Introduction to Machine Learning FREE CHAPTER

2. Classification

3. Clustering

4. Model Selection and Regularization

5. Nonlinearity

6. Supervised Learning

7. Unsupervised Learning

8. Reinforcement Learning

9. Structured Prediction

10. Neural Networks

11. Deep Learning

12. Case Study - Exploring World Bank Data

Exploring World Bank data

13. Case Study - Pricing Reinsurance Contracts

Pricing reinsurance contracts

14. Case Study - Forecast of Electricity Consumption

Tobit regression - measuring the students' academic aptitude

Let us measure the academic aptitude of a student on a scale of 200-800. This measurement is based on the model using reading and math scores. The nature of the program in which the student has been enrolled is also to be taken into consideration. There are three types of programs: academic, general, and vocational. The problem is that some students may answer all the questions on the academic aptitude test correctly and score 800 even though it is likely that these students are not truly equal in aptitude. This may be true for all the students who may answer all the questions incorrectly and score 200.

Getting ready

In order to complete this recipe we shall be using a student's dataset. The first step is collecting the data.

Step 1 - collecting data

To develop the Tobit regression model we shall use the student dataset titled tobit, which is available at http://www.ats.ucla.edu/stat/data/tobit.csv in an MS Excel format. There are...

The rest of the chapter is locked

Register for a free Packt account to unlock a world of extra content!

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at €18.99/month. Cancel anytime

Authors (1)

Atul Tripathi

Atul Tripathi

Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.

See other products by Atul Tripathi

Other recommended products

Related to this chapter

Regression Analysis with R

Regression Analysis with R

Regression analysis is a statistical process which enables prediction of relationships between variables. This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. Each chapter starts with explaining the theoretical concepts and once the reader gets comfortable with the theory, we move to the practical examples to support the understanding. By the end of this book you will know all the concepts and pain-points related to regression analysis, and you will be able to implement your learning in your projects.

Jan 2018 14h 4m

Regression Analysis with R

Regression Analysis with R

Regression analysis is a statistical process which enables prediction of relationships between variables. This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. Each chapter starts with explaining the theoretical concepts and once the reader gets comfortable with the theory, we move to the practical examples to support the understanding. By the end of this book you will know all the concepts and pain-points related to regression analysis, and you will be able to implement your learning in your projects.

Jan 2018 14h 4m

Regression Analysis with R

Regression Analysis with R

Regression analysis is a statistical process which enables prediction of relationships between variables. This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. Each chapter starts with explaining the theoretical concepts and once the reader gets comfortable with the theory, we move to the practical examples to support the understanding. By the end of this book you will know all the concepts and pain-points related to regression analysis, and you will be able to implement your learning in your projects.

Jan 2018 14h 4m

Machine Learning with R Cookbook

Machine Learning with R Cookbook

The R language is a powerful open source functional programming language. At its core, R is a statistical language that provides impressive tools to analyze data and create high-level graphics. This book covers the basics of R by setting up a user-friendly programming environment and programming ETL in R. Data exploration examples are provided that demonstrate how powerful data visualisation and machine learning is in discovering hidden relationships. You will also explore air quality data, steps to fix the missing values and visualising the same. You will then dive into important machine learning topics, including data classification, regression, survival analysis, time series analysis, clustering association rule mining, and dimension reduction.This book will include the latest code and examples based on R 3.3 and above—updated for better computation, accuracy, and speed with R.

Oct 2017 19h 4m

Statistical Application Development with R and Python

Statistical Application Development with R and Python

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.

Aug 2017 14h 24m

Statistical Application Development with R and Python

Statistical Application Development with R and Python

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.

Aug 2017 14h 24m

Statistical Application Development with R and Python

Statistical Application Development with R and Python

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.

Aug 2017 14h 24m

Statistical Application Development with R and Python

Statistical Application Development with R and Python

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.

Aug 2017 14h 24m

Neural Networks with R

Neural Networks with R

The book helps you learn neural networks and implement them in R. It covers real-world use cases that will help you better understand their concepts. A basic understanding of R and mathematics is required.

Neural Networks with R

Neural Networks with R

The book helps you learn neural networks and implement them in R. It covers real-world use cases that will help you better understand their concepts. A basic understanding of R and mathematics is required.

Neural Networks with R

Neural Networks with R

The book helps you learn neural networks and implement them in R. It covers real-world use cases that will help you better understand their concepts. A basic understanding of R and mathematics is required.

Neural Networks with R

Neural Networks with R

The book helps you learn neural networks and implement them in R. It covers real-world use cases that will help you better understand their concepts. A basic understanding of R and mathematics is required.

Personalised recommendations for you

Based on your interests and search pattern

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m