Machine Learning Algorithms For Data Scientists An Overview

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Machine Learning Algorithms for Data Scientists: An Overview

Vinaitheerthan Renganathan
Machine Learning Algorithms for Data Scientists An Overview Book PDF

Author : Vinaitheerthan Renganathan
Publisher : Vinaitheerthan Renganathan
Page : 102 pages
File Size : 45,8 Mb
Release : 2021-06-02
Category : Computers
ISBN : 9789354737695

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Machine Learning Algorithms for Data Scientists: An Overview by Vinaitheerthan Renganathan Pdf

Machine Learning models are widely used in different fields such as Artificial Intelligence, Business, Clinical and Biological Sciences which includes self-driving cars, predictive models, disease prediction, genome sequencing, spam filtering, product recommendation, fraud detection and image recognition . It has gained importance due to its capabilities of handling large volume of data, prediction and classification accuracy and validation procedures. Machine Learning models are built on the basis of statistical and mathematical algorithms. One important aspect of machine learning is it does not stick to standard algorithm throughout modeling process instead it learns from the data over a period of time and improves the accuracy of the model. Classification and prediction tasks are carried out based on the characteristics, patterns and relationship of the features present in the data set. Machine learning model also forms the basis of Deep Learning models. Machine Learning models involve supervised learning, unsupervised learning, semi supervised learning and reinforcement learning algorithms. Data Scientists analyze, model and visualize data and provide actionable insights to the decision makers. Machine learning algorithms and tools help the data scientist to carry out these tasks with the help of software such R and Python. This book provides an overview of Machine Learning models, algorithms and its application in different fields through the use of R Software. It also provides short introduction to R software for the benefit of users. Author assumes the users have basic descriptive and inferential statistical knowledge which is essential for building Machine Learning models. Data sets used in the books can be downloaded from the author’s website.

Introduction to Data Science

Rafael A. Irizarry
Introduction to Data Science Book PDF

Author : Rafael A. Irizarry
Publisher : CRC Press
Page : 794 pages
File Size : 54,9 Mb
Release : 2019-11-20
Category : Mathematics
ISBN : 9781000708035

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Introduction to Data Science by Rafael A. Irizarry Pdf

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

Introduction to Data Science and Machine Learning

Keshav Sud,Pakize Erdogmus,Seifedine Kadry
Introduction to Data Science and Machine Learning Book PDF

Author : Keshav Sud,Pakize Erdogmus,Seifedine Kadry
Publisher : BoD – Books on Demand
Page : 233 pages
File Size : 49,9 Mb
Release : 2020-03-25
Category : Computers
ISBN : 9781838803339

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Introduction to Data Science and Machine Learning by Keshav Sud,Pakize Erdogmus,Seifedine Kadry Pdf

Introduction to Data Science and Machine Learning has been created with the goal to provide beginners seeking to learn about data science, data enthusiasts, and experienced data professionals with a deep understanding of data science application development using open-source programming from start to finish. This book is divided into four sections: the first section contains an introduction to the book, the second covers the field of data science, software development, and open-source based embedded hardware; the third section covers algorithms that are the decision engines for data science applications; and the final section brings together the concepts shared in the first three sections and provides several examples of data science applications.

Introduction to Data Science

Peters Morgan
Introduction to Data Science Book PDF

Author : Peters Morgan
Publisher : Unknown
Page : 118 pages
File Size : 51,6 Mb
Release : 2017-04-07
Category : Electronic
ISBN : 1722799579

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Introduction to Data Science by Peters Morgan Pdf

******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of learning data science with easiest way (For Beginners)? If you are looking for a complete introduction to data science, this book is for you.After his great success with his first book "Data Analysis from Scratch with Python", Peters Morgan publish this book focusing now in data science and machine learning. Practitioners consider it as the easiest guide ever written in this domain. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses.To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples This book is an introduction to the main concepts of data science explained with easiest examples. Peters Morgan focus on the practical aspects of using data science and machine learning algorithms, rather than the math behind them. Target Users Target UsersThe book is designed for a variety of target audiences. The most suitable users would include: Beginners who want to approach data science, but are too afraid of complex math to start Newbies in computer science techniques and data science Professionals in data science and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on data science What's Inside This Book? Introduction Statistics Probability Bayes' Theorem and Naïve Bayes Algorithm Asking the Right Question Data Acquisition Data Preparation Data Exploration Data Modelling Data Presentation Supervised Learning Algorithms Unsupervised Learning Algorithms Semi-supervised Learning Algorithms Reinforcement Learning Algorithms Overfitting and Underfitting Correctness The Bias-Variance Trade-off Feature Extraction and Selection K-Nearest Neighbors Naive Bayes Simple and Multiple Linear Regression Logistic Regression GLM models Decision Trees and Random forest Perceptrons Backpropagation Clustering Natural Language Processing Frequently Asked Questions Q: Is this book for me and do I need programming experience?A: No programming experience is required. This book is an introduction to data science without any type of programming.Q: Does this book include everything I need to become a data science expert?A: Unfortunately, no. This book is designed for readers taking their first steps in data science and machine learning and further learning will be required beyond this book to master all aspects.Q: Can I loan this book to friends?A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days.Q: Can I have a refund if this book is not fitted for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected].

Data Science and Machine Learning

Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman
Data Science and Machine Learning Book PDF

Author : Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman
Publisher : CRC Press
Page : 538 pages
File Size : 54,6 Mb
Release : 2019-11-20
Category : Business & Economics
ISBN : 9781000730777

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Data Science and Machine Learning by Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman Pdf

Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

Machine Learning Algorithms

Giuseppe Bonaccorso
Machine Learning Algorithms Book PDF

Author : Giuseppe Bonaccorso
Publisher : Packt Publishing Ltd
Page : 514 pages
File Size : 55,6 Mb
Release : 2018-08-30
Category : Mathematics
ISBN : 9781789345483

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Machine Learning Algorithms by Giuseppe Bonaccorso Pdf

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and make predictions across various datasets Book Description Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative. What you will learn Study feature selection and the feature engineering process Assess performance and error trade-offs for linear regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector Machines (SVM) Explore the concept of natural language processing (NLP) and recommendation systems Create a machine learning architecture from scratch Who this book is for Machine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book.

Machine Learning Algorithms

Giuseppe Bonaccorso
Machine Learning Algorithms Book PDF

Author : Giuseppe Bonaccorso
Publisher : Packt Publishing Ltd
Page : 360 pages
File Size : 52,8 Mb
Release : 2017-07-24
Category : Computers
ISBN : 9781785884511

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Machine Learning Algorithms by Giuseppe Bonaccorso Pdf

Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.

Introduction to Machine Learning with Python

Andreas C. Müller,Sarah Guido
Introduction to Machine Learning with Python Book PDF

Author : Andreas C. Müller,Sarah Guido
Publisher : "O'Reilly Media, Inc."
Page : 400 pages
File Size : 49,9 Mb
Release : 2016-09-26
Category : Computers
ISBN : 9781449369897

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Introduction to Machine Learning with Python by Andreas C. Müller,Sarah Guido Pdf

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills

Machine Learning

Peter Flach
Machine Learning Book PDF

Author : Peter Flach
Publisher : Cambridge University Press
Page : 415 pages
File Size : 55,8 Mb
Release : 2012-09-20
Category : Computers
ISBN : 9781107096394

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Machine Learning by Peter Flach Pdf

Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.

Introduction to Algorithms for Data Mining and Machine Learning

Xin-She Yang
Introduction to Algorithms for Data Mining and Machine Learning Book PDF

Author : Xin-She Yang
Publisher : Academic Press
Page : 188 pages
File Size : 50,8 Mb
Release : 2019-07-15
Category : Mathematics
ISBN : 9780128172162

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Introduction to Algorithms for Data Mining and Machine Learning by Xin-She Yang Pdf

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

Data Science for Beginners

Prof John Smith
Data Science for Beginners Book PDF

Author : Prof John Smith
Publisher : Independently Published
Page : 83 pages
File Size : 48,5 Mb
Release : 2018-12-12
Category : Electronic
ISBN : 1791620124

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Data Science for Beginners by Prof John Smith Pdf

DATA SCIENCE FOR BEGINNERS Introduction to Data Science: Python,Coding, Application, Statistics,Decision Tree, Neural Network, and Linear Algebra WHAT THIS BOOK WILL DO FOR YOU We will talk about what is the need for data science and then what exactly is data science some definitions and understand. The differences between data science and business intelligence,Then we will talk about the prerequisites for learning data science, and then what does the data scientist do. What are the activities performed by a data scientist as a part of his daily life and then we will talk about the data science lifecycle witha quick example and briefly touch upon the demand or ever-increasing demand for data scientist. Benefits of Data science Data Science: Automobile Data science: Aviation Data science can also be used to make promotional offers. Chapters Data science: Its Advantage Data science: Its Definition Process in data science Difference between business intelligence and data science Prerequisites for data science Machine learning. Data science: Tools and skills in data science. Data Science: Machine-learning algorithms Data science: Life cycle of a data science Data science: Exploratory data analysis Data science: Techniques for exploratory data analysis

Advances in Machine Learning and Data Science

Damodar Reddy Edla,Pawan Lingras,Venkatanareshbabu K.
Advances in Machine Learning and Data Science Book PDF

Author : Damodar Reddy Edla,Pawan Lingras,Venkatanareshbabu K.
Publisher : Springer
Page : 380 pages
File Size : 53,9 Mb
Release : 2018-05-16
Category : Technology & Engineering
ISBN : 9789811085697

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Advances in Machine Learning and Data Science by Damodar Reddy Edla,Pawan Lingras,Venkatanareshbabu K. Pdf

The Volume of “Advances in Machine Learning and Data Science - Recent Achievements and Research Directives” constitutes the proceedings of First International Conference on Latest Advances in Machine Learning and Data Science (LAMDA 2017). The 37 regular papers presented in this volume were carefully reviewed and selected from 123 submissions. These days we find many computer programs that exhibit various useful learning methods and commercial applications. Goal of machine learning is to develop computer programs that can learn from experience. Machine learning involves knowledge from various disciplines like, statistics, information theory, artificial intelligence, computational complexity, cognitive science and biology. For problems like handwriting recognition, algorithms that are based on machine learning out perform all other approaches. Both machine learning and data science are interrelated. Data science is an umbrella term to be used for techniques that clean data and extract useful information from data. In field of data science, machine learning algorithms are used frequently to identify valuable knowledge from commercial databases containing records of different industries, financial transactions, medical records, etc. The main objective of this book is to provide an overview on latest advancements in the field of machine learning and data science, with solutions to problems in field of image, video, data and graph processing, pattern recognition, data structuring, data clustering, pattern mining, association rule based approaches, feature extraction techniques, neural networks, bio inspired learning and various machine learning algorithms.

Data Science Algorithms in a Week

Dávid Natingga
Data Science Algorithms in a Week Book PDF

Author : Dávid Natingga
Publisher : Packt Publishing Ltd
Page : 214 pages
File Size : 51,6 Mb
Release : 2018-10-31
Category : Computers
ISBN : 9781789800968

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Data Science Algorithms in a Week by Dávid Natingga Pdf

Build a strong foundation of machine learning algorithms in 7 days Key FeaturesUse Python and its wide array of machine learning libraries to build predictive models Learn the basics of the 7 most widely used machine learning algorithms within a weekKnow when and where to apply data science algorithms using this guideBook Description Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem What you will learnUnderstand how to identify a data science problem correctlyImplement well-known machine learning algorithms efficiently using PythonClassify your datasets using Naive Bayes, decision trees, and random forest with accuracyDevise an appropriate prediction solution using regressionWork with time series data to identify relevant data events and trendsCluster your data using the k-means algorithmWho this book is for This book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You’ll also find this book useful if you’re currently working with data science algorithms in some capacity and want to expand your skill set

Practical Machine Learning for Data Analysis Using Python

Abdulhamit Subasi
Practical Machine Learning for Data Analysis Using Python Book PDF

Author : Abdulhamit Subasi
Publisher : Academic Press
Page : 534 pages
File Size : 55,5 Mb
Release : 2020-06-05
Category : Computers
ISBN : 9780128213803

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Practical Machine Learning for Data Analysis Using Python by Abdulhamit Subasi Pdf

Practical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data Explores important classification and regression algorithms as well as other machine learning techniques Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features

Data Scientist Pocket Guide

Mohamed Sabri
Data Scientist Pocket Guide Book PDF

Author : Mohamed Sabri
Publisher : BPB Publications
Page : 418 pages
File Size : 53,8 Mb
Release : 2021-06-24
Category : Computers
ISBN : 9789390684977

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Data Scientist Pocket Guide by Mohamed Sabri Pdf

Discover one of the most complete dictionaries in data science. KEY FEATURES ● Simplified understanding of complex concepts, terms, terminologies, and techniques. ● Combined glossary of machine learning, mathematics, and statistics. ● Chronologically arranged A-Z keywords with brief description. DESCRIPTION This pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics. The extensive list of glossaries comprises concepts, processes, algorithms, data structures, techniques, and many more. Each of these terms is explained in the simplest words possible. This pocket guide will help you to stay up to date of the most essential terms and references used in the process of data analysis and machine learning. WHAT YOU WILL LEARN ● Get absolute clarity on every concept, process, and algorithm used in the process of data science operations. ● Keep yourself technically strong and sound-minded during data science meetings. ● Strengthen your knowledge in the field of Big data and business intelligence. WHO THIS BOOK IS FOR This book is for data professionals, data scientists, students, or those who are new to the field who wish to stay on top of industry jargon and terminologies used in the field of data science. TABLE OF CONTENTS 1. Chapter one: A 2. Chapter two: B 3. Chapter three: C 4. Chapter four: D 5. Chapter five: E 6. Chapter six: F 7. Chapter seven: G 8. Chapter eight: H 9. Chapter nine: I 10. Chapter ten: J 11. Chapter 11: K 12. Chapter 12: L 13. Chapter 13: M 14. Chapter 14: N 15. Chapter 15: O 16. Chapter 16: P 17. Chapter 17: Q 18. Chapter 18: R 19. Chapter 19 : S 20. Chapter 20 : T 21. Chapter 21 : U 22. Chapter 22 : V 23. Chapter 23: W 24. Chapter 24: X 25. Chapter 25: Y 26. Chapter 26 : Z