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Practical Machine Learning Cookbook
Practical Machine Learning Cookbook

Practical Machine Learning Cookbook: Supervised and unsupervised machine learning simplified

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Practical Machine Learning Cookbook

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...

Multinomial logistic regression - understanding program choices made by students


Let's assume that high school students are to be enrolled on a program. The students are given the opportunity to choose programs of their choice. The choices of the students are based on three options. These choices are general program, vocational program, and academic program. The choice of each student is based on each student's writing score and social economic status.

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

The student's dataset titled hsbdemo is being utilized. The dataset is available at: http://voia.yolasite.com/resources/hsbdemo.csv in an MS Excel format. There are 201 data rows and 13 variables in the dataset. The eight numeric measurements are as follows:

  • id
  • read
  • write
  • math
  • science
  • socst
  • awards
  • cid

The non-numeric measurements are as follows:

  • gender
  • ses
  • schtyp
  • prog
  • honors

How to do it...

Let's get into the...

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...

Poisson regression - understanding species present in Galapagos Islands


The Galapagos Islands are situated in the Pacific Ocean about 1000 km from the Ecuadorian coast. The archipelago consists of 13 islands, five of which are inhabited. The islands are rich in flora and fauna. Scientists are still perplexed by the fact that such a diverse set of species can flourish in such a small and remote group of islands.

Getting ready

In order to complete this recipe we shall be using species dataset. The first step is collecting the data.

Step 1 - collecting and describing the data

We will utilize the number of species dataset titled gala that is available at https://github.com/burakbayramli/kod/blob/master/books/Practical_Regression_Anove_Using_R_Faraway/gala.txt .

The dataset includes 30 cases and seven variables in the dataset. The seven numeric measurements include the following:

  • Species
  • Endemics
  • Area
  • Elevation
  • Nearest
  • Scruz
  • Adjcacent

How to do it...

Let's get into the details.

Step 2 - exploring the data...

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...

Poisson regression - understanding species present in Galapagos Islands

The Galapagos Islands are situated in the Pacific Ocean about 1000 km from the Ecuadorian coast. The archipelago consists of 13 islands, five of which are inhabited. The islands are rich in flora and fauna. Scientists are still perplexed by the fact that such a diverse set of species can flourish in such a small and remote group of islands.

Getting ready

In order to complete this recipe we shall be using species dataset. The first step is collecting the data.

Step 1 - collecting and describing the data

We will utilize the number of species dataset titled gala that is available at https://github.com/burakbayramli/kod/blob/master/books/Practical_Regression_Anove_Using_R_Faraway/gala.txt .

The dataset includes 30 cases and seven variables in the dataset. The seven numeric measurements include the following:

  • Species
  • Endemics
  • Area
  • Elevation
  • Nearest
  • Scruz
  • Adjcacent

How to do it...

Let's get into the details.

Step 2 - exploring the...

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Key benefits

  • • Implement a wide range of algorithms and techniques for tackling complex data
  • • Improve predictions and recommendations to have better levels of accuracy
  • • Optimize performance of your machine-learning systems

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.

Who is this book for?

This book is for analysts, statisticians, and data scientists with knowledge of fundamentals of machine learning and statistics, who need help in dealing with challenging scenarios faced every day of working in the field of machine learning and improving system performance and accuracy. It is assumed that as a reader you have a good understanding of mathematics. Working knowledge of R is expected.

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
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Length: 570 pages
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ISBN-13 : 9781785280511
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Amazon
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Table of Contents

14 Chapters
1. Introduction to Machine Learning Chevron down icon Chevron up icon
2. Classification Chevron down icon Chevron up icon
3. Clustering Chevron down icon Chevron up icon
4. Model Selection and Regularization Chevron down icon Chevron up icon
5. Nonlinearity Chevron down icon Chevron up icon
6. Supervised Learning Chevron down icon Chevron up icon
7. Unsupervised Learning Chevron down icon Chevron up icon
8. Reinforcement Learning Chevron down icon Chevron up icon
9. Structured Prediction Chevron down icon Chevron up icon
10. Neural Networks Chevron down icon Chevron up icon
11. Deep Learning Chevron down icon Chevron up icon
12. Case Study - Exploring World Bank Data Chevron down icon Chevron up icon
13. Case Study - Pricing Reinsurance Contracts Chevron down icon Chevron up icon
14. Case Study - Forecast of Electricity Consumption Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(1 Ratings)
5 star 0%
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3 star 100%
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1 star 0%
Ram Jun 12, 2017
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
So far explanations are not easy to understand & steps are skipped.
Amazon Verified review Amazon
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