Predictive analytics: Data Mining, Machine Learning, and Data Science for Practitioners
(PREDANA.AP1)
/ ISBN: 9781644593264
Predictive analytics: Data Mining, Machine Learning, and Data Science for Practitioners
Predictive analytics is all about foreseeing the future and making smarter and faster business decisions. Business analytics is often characterized by three levels/echelons representing the hierarchical nature of the term—descriptive, predictive, and prescriptive. Organizations usually start with descriptive analytics, then move into predictive analytics, and finally reach prescriptive analytics. Learn predictive analytics with uCertify's course Predictive analytics: Data Mining, Machine Learning, and Data Science for Practitioners. The course has well descriptive interactive lessons containing pre and postassessment questions, knowledge checks, quizzes, flashcards, and glossary terms to get a detailed understanding of predictive analytics.
Lessons

12+ Lessons

134+ Exercises

135+ Quizzes

105+ Flashcards

105+ Glossary of terms
TestPrep

66+ Pre Assessment Questions

66+ Post Assessment Questions
LiveLab

10+ LiveLab

10+ Video tutorials

01:15+ Hours
Video Lessons

45+ Videos

08:49+ Hours
 About This eBook
 Foreword
 What’s in a Name?
 Why the Sudden Popularity of Analytics and Data Science?
 The Application Areas of Analytics
 The Main Challenges of Analytics
 A Longitudinal View of Analytics
 A Simple Taxonomy for Analytics
 The Cutting Edge of Analytics: IBM Watson
 Summary
 References
 What Is Data Mining?
 What Data Mining Is Not
 The Most Common Data Mining Applications
 What Kinds of Patterns Can Data Mining Discover?
 Popular Data Mining Tools
 The Dark Side of Data Mining: Privacy Concerns
 Summary
 References
 The Knowledge Discovery in Databases (KDD) Process
 CrossIndustry Standard Process for Data Mining (CRISPDM)
 SEMMA
 SEMMA Versus CRISPDM
 Six Sigma for Data Mining
 Which Methodology Is Best?
 Summary
 References
 The Nature of Data in Data Analytics
 Preprocessing of Data for Analytics
 Data Mining Methods
 Prediction
 Classification
 Decision Trees
 Cluster Analysis for Data Mining
 kMeans Clustering Algorithm
 Association
 Apriori Algorithm
 Data Mining and Predictive Analytics Misconceptions and Realities
 Summary
 References
 Naive Bayes
 Nearest Neighbor
 Similarity Measure: The Distance Metric
 Artificial Neural Networks
 Support Vector Machines
 Linear Regression
 Logistic Regression
 TimeSeries Forecasting
 Summary
 References
 Model Ensembles
 Bias–Variance Tradeoff in Predictive Analytics
 Imbalanced Data Problems in Predictive Analytics
 Explainability of Machine Learning Models for Predictive Analytics
 Summary
 References
 Natural Language Processing
 Text Mining Applications
 The Text Mining Process
 Text Mining Tools
 Topic Modeling
 Sentiment Analysis
 Summary
 References
 Where Does Big Data Come From?
 The Vs That Define Big Data
 Fundamental Concepts of Big Data
 The Business Problems That Big Data Analytics Addresses
 Big Data Technologies
 Data Scientists
 Big Data and Stream Analytics
 Data Stream Mining
 Summary
 References
 Introduction to Deep Learning
 Basics of “Shallow” Neural Networks
 Elements of an Artificial Neural Network
 Deep Neural Networks
 Convolutional Neural Networks
 Recurrent Networks and Long ShortTerm Memory Networks
 Computer Frameworks for Implementation of Deep Learning
 Cognitive Computing
 Summary
 References
 Project Constraints: Time and Money
 The Learning Curve
 The KNIME Community
 Correctness and Flexibility
 Extensive Coverage of Data Science Techniques
 Data Science in the Enterprise
 Summary and Conclusions
 Acknowledgment
 Introduction to Predictive Analytics
 Introduction to Predictive Analytics and Data Mining
 The Data Mining Process
 Data and Methods in Data Mining
 Data Mining Algorithms
 Text Analytics and Text Mining
 Big Data Analytics
 Predictive Analytics Best Practices
 Summary
Hands on Activities (Live Labs)
 Creating a Decision Tree in Python
 Creating a Decision Tree in KNIME
 Running kMeans Clustering Algorithm in KNIME
 Using the kNearest Neighbor Algorithm
 Using ANN and SVM for Prediction Type Analytics Problems
 Implementing Linear Regression in Python
 Implementing Linear Regression Model in KNIME
 Showcasing Better Practices With a Customer Churn Analysis
 Performing Topic Modeling
 Performing Sentiment Analysis
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