Machine Learning for Business Analytics Concepts Techniques and Applications with JMP Pro 2nd Edition by Galit Shmueli, Peter Bruce, Mia Stephens, Muralidhara Anandamurthy, Nitin Patel ISBN 9781119903857 1119903858

Machine Learning for Business Analytics Concepts Techniques and Applications with JMP Pro 2nd Edition by Galit Shmueli, Peter Bruce, Mia Stephens, Muralidhara Anandamurthy, Nitin Patel  – Ebook PDF Instant Download/Delivery. 9781119903857 ,1119903858
Full download Machine Learning for Business Analytics Concepts Techniques and Applications with JMP Pro 2nd Edition after payment

Product details:
ISBN 10: 1119903858
ISBN 13: 9781119903857
Author: Galit Shmueli, Peter Bruce, Mia Stephens, Muralidhara Anandamurthy, Nitin Patel 

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro^®, 2^nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing users’ understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses.

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro^®, 2^nd ed. readers will also find:

• Updated material which improves the book’s usefulness as a reference for professionals beyond the classroom
• Four new chapters, covering topics including Text Mining and Responsible Data Science
• An updated companion website with data sets and other instructor resources: www.jmp.com/dataminingbook
• A guide to JMP Pro^®’s new features and enhanced functionality

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro^®, 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.

Machine Learning for Business Analytics Concepts Techniques and Applications with JMP Pro 2nd Edition Table of contents:

PART I: PRELIMINARIES

1 INTRODUCTION

1.1 WHAT IS BUSINESS ANALYTICS?

1.2 WHAT IS MACHINE LEARNING?

1.3 MACHINE LEARNING, AI, AND RELATED TERMS

1.4 BIG DATA

1.5 DATA SCIENCE

1.6 WHY ARE THERE SO MANY DIFFERENT METHODS?

1.7 TERMINOLOGY AND NOTATION

1.8 ROAD MAPS TO THIS BOOK

2 OVERVIEW OF THE MACHINE LEARNING PROCESS

2.1 INTRODUCTION

2.2 CORE IDEAS IN MACHINE LEARNING

2.3 THE STEPS IN A MACHINE LEARNING PROJECT

2.4 PRELIMINARY STEPS

2.5 PREDICTIVE POWER AND OVERFITTING

2.6 BUILDING A PREDICTIVE MODEL WITH JMP Pro

2.7 USING JMP Pro FOR MACHINE LEARNING

2.8 AUTOMATING MACHINE LEARNING SOLUTIONS

2.9 ETHICAL PRACTICE IN MACHINE LEARNING

NOTES

PART II: DATA EXPLORATION AND DIMENSION REDUCTION

3 DATA VISUALIZATION

3.1 INTRODUCTION

3.2 DATA EXAMPLES

3.3 BASIC CHARTS: BAR CHARTS, LINE GRAPHS, AND SCATTER PLOTS

3.4 MULTIDIMENSIONAL VISUALIZATION

3.5 SPECIALIZED VISUALIZATIONS

3.6 SUMMARY: MAJOR VISUALIZATIONS AND OPERATIONS, ACCORDING TO MACHINE LEARNING GOAL

NOTES

4 DIMENSION REDUCTION

4.1 INTRODUCTION

4.2 CURSE OF DIMENSIONALITY

4.3 PRACTICAL CONSIDERATIONS

4.4 DATA SUMMARIES

4.5 CORRELATION ANALYSIS

4.6 REDUCING THE NUMBER OF CATEGORIES IN CATEGORICAL VARIABLES

4.7 CONVERTING A CATEGORICAL VARIABLE TO A CONTINUOUS VARIABLE

4.8 PRINCIPAL COMPONENT ANALYSIS

4.9 DIMENSION REDUCTION USING REGRESSION MODELS

4.10 DIMENSION REDUCTION USING CLASSIFICATION AND REGRESSION TREES

NOTES

PART III: PERFORMANCE EVALUATION

5 EVALUATING PREDICTIVE PERFORMANCE

5.1 INTRODUCTION

5.2 EVALUATING PREDICTIVE PERFORMANCE

5.3 JUDGING CLASSIFIER PERFORMANCE

5.4 JUDGING RANKING PERFORMANCE

5.5 OVERSAMPLING

PART IV: PREDICTION AND CLASSIFICATION METHODS

6 MULTIPLE LINEAR REGRESSION

6.1 INTRODUCTION

6.2 EXPLANATORY VS. PREDICTIVE MODELING

6.3 ESTIMATING THE REGRESSION EQUATION AND PREDICTION

6.4 VARIABLE SELECTION IN LINEAR REGRESSION

NOTES

7 k‐NEAREST NEIGHBORS (k‐NN)

7.1 THE k‐NN CLASSIFIER (CATEGORICAL OUTCOME)

7.2 K‐NN FOR A NUMERICAL RESPONSE

7.3 ADVANTAGES AND SHORTCOMINGS OF K‐NN ALGORITHMS

NOTES

8 THE NAIVE BAYES CLASSIFIER

8.1 INTRODUCTION

8.2 APPLYING THE FULL (EXACT) BAYESIAN CLASSIFIER

8.3 SOLUTION: NAIVE BAYES

8.4 ADVANTAGES AND SHORTCOMINGS OF THE NAIVE BAYES CLASSIFIER

9 CLASSIFICATION AND REGRESSION TREES

9.1 INTRODUCTION

9.2 CLASSIFICATION TREES

9.3 GROWING A TREE FOR RIDING MOWERS EXAMPLE

9.4 EVALUATING THE PERFORMANCE OF A CLASSIFICATION TREE

9.5 AVOIDING OVERFITTING

9.6 CLASSIFICATION RULES FROM TREES

9.7 CLASSIFICATION TREES FOR MORE THAN TWO CLASSES

9.8 REGRESSION TREES

9.9 ADVANTAGES AND WEAKNESSES OF A SINGLE TREE

9.10 IMPROVING PREDICTION: RANDOM FORESTS AND BOOSTED TREES

NOTES

10 LOGISTIC REGRESSION

10.1 INTRODUCTION

10.2 THE LOGISTIC REGRESSION MODEL

10.3 EXAMPLE: ACCEPTANCE OF PERSONAL LOAN

10.4 EVALUATING CLASSIFICATION PERFORMANCE

10.5 VARIABLE SELECTION

10.6 LOGISTIC REGRESSION FOR MULTI‐CLASS CLASSIFICATION

10.7 EXAMPLE OF COMPLETE ANALYSIS: PREDICTING DELAYED FLIGHTS

Notes

11 NEURAL NETS

11.1 INTRODUCTION

11.2 CONCEPT AND STRUCTURE OF A NEURAL NETWORK

11.3 FITTING A NETWORK TO DATA

11.4 USER INPUT IN JMP Pro

11.5 EXPLORING THE RELATIONSHIP BETWEEN PREDICTORS AND OUTCOME

11.6 DEEP LEARNING

11.7 ADVANTAGES AND WEAKNESSES OF NEURAL NETWORKS

NOTES

12 DISCRIMINANT ANALYSIS

12.1 INTRODUCTION

12.2 DISTANCE OF AN OBSERVATION FROM A CLASS

12.3 FROM DISTANCES TO PROPENSITIES AND CLASSIFICATIONS

12.4 CLASSIFICATION PERFORMANCE OF DISCRIMINANT ANALYSIS

12.5 PRIOR PROBABILITIES

12.6 CLASSIFYING MORE THAN TWO CLASSES

12.7 ADVANTAGES AND WEAKNESSES

NOTES

13 GENERATING, COMPARING, AND COMBINING MULTIPLE MODELS

13.1 ENSEMBLES

13.2 AUTOMATED MACHINE LEARNING (AUTOML)

13.3 SUMMARY

NOTE

PART V: INTERVENTION AND USER FEEDBACK

14 INTERVENTIONS: EXPERIMENTS, UPLIFT MODELS, AND REINFORCEMENT LEARNING

14.1 INTRODUCTION

14.2 A/B TESTING

14.3 UPLIFT (PERSUASION) MODELING

14.4 REINFORCEMENT LEARNING

14.5 SUMMARY

NOTES

PART VI: MINING RELATIONSHIPS AMONG RECORDS

15 ASSOCIATION RULES AND COLLABORATIVE FILTERING

15.1 ASSOCIATION RULES

15.2 COLLABORATIVE FILTERING

15.3 SUMMARY

NOTES

16 CLUSTER ANALYSIS

16.1 INTRODUCTION

16.2 MEASURING DISTANCE BETWEEN TWO RECORDS

16.3 MEASURING DISTANCE BETWEEN TWO CLUSTERS

16.4 HIERARCHICAL (AGGLOMERATIVE) CLUSTERING

16.5 NONHIERARCHICAL CLUSTERING: THE K‐MEANS ALGORITHM

NOTE

PART VII: FORECASTING TIME SERIES

17 HANDLING TIME SERIES

17.1 INTRODUCTION

17.2 DESCRIPTIVE VS. PREDICTIVE MODELING

17.3 POPULAR FORECASTING METHODS IN BUSINESS

17.4 TIME SERIES COMPONENTS

17.5 DATA PARTITIONING AND PERFORMANCE EVALUATION

NOTES

18 REGRESSION‐BASED FORECASTING

18.1 A MODEL WITH TREND

18.2 A MODEL WITH SEASONALITY

18.3 A MODEL WITH TREND AND SEASONALITY

18.4 AUTOCORRELATION AND ARIMA MODELS

Notes

19 SMOOTHING AND DEEP LEARNING METHODS FOR FORECASTING

19.1 INTRODUCTION

19.2 MOVING AVERAGE

19.3 SIMPLE EXPONENTIAL SMOOTHING

19.4 ADVANCED EXPONENTIAL SMOOTHING

19.5 DEEP LEARNING FOR FORECASTING

NOTES

PART VIII: DATA ANALYTICS

20 TEXT MINING

20.1 INTRODUCTION

20.2 THE TABULAR REPRESENTATION OF TEXT: DOCUMENT–TERM MATRIX AND “BAG‐OF‐WORDS”

20.3 BAG‐OF‐WORDS VS. MEANING EXTRACTION AT DOCUMENT LEVEL

20.4 PREPROCESSING THE TEXT

20.5 IMPLEMENTING MACHINE LEARNING METHODS

20.6 EXAMPLE: ONLINE DISCUSSIONS ON AUTOS AND ELECTRONICS

20.7 EXAMPLE: SENTIMENT ANALYSIS OF MOVIE REVIEWS

20.8 SUMMARY

NOTES

21 RESPONSIBLE DATA SCIENCE

21.1 INTRODUCTION

21.2 UNINTENTIONAL HARM

21.3 LEGAL CONSIDERATIONS

21.4 PRINCIPLES OF RESPONSIBLE DATA SCIENCE

21.5 A RESPONSIBLE DATA SCIENCE FRAMEWORK

21.6 DOCUMENTATION TOOLS

21.7 EXAMPLE: APPLYING THE RDS FRAMEWORK TO THE COMPAS EXAMPLE

21.8 SUMMARY

NOTES

PART IX: CASES

22 CASES

22.1 CHARLES BOOK CLUB

22.2 GERMAN CREDIT

22.3 TAYKO SOFTWARE CATALOGER

22.4 POLITICAL PERSUASION

22.5 TAXI CANCELLATIONS

22.6 SEGMENTING CONSUMERS OF BATH SOAP

22.7 CATALOG CROSS‐SELLING

22.8 DIRECT‐MAIL FUNDRAISING

22.9 TIME SERIES CASE: FORECASTING PUBLIC TRANSPORTATION DEMAND

22.10 LOAN APPROVAL

NOTES

REFERENCES

DATA FILES USED IN THE BOOK

INDEX

People also search for Machine Learning for Business Analytics Concepts Techniques and Applications with JMP Pro 2nd Edition:

machine learning for dummies 2nd edition

machine learning for business

business analytics 2

data analytics for decision making course