Predictive Analytics, 2nd Edition

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Introduction

001. Predictive Analytics Introduction.mp4

02:54

Lesson 1 Introduction to Predictive Analytics

001. Topics.mp4

01:05

002. 1.1 What Is Analytics and Where Does Data Mining Fit In.mp4

13:49

003. 1.2 Popularity and Application Areas of Analytics.mp4

13:58

004. 1.3 An Analytics Timeline and a Simple Taxonomy.mp4

14:04

005. 1.4 Cutting Edge of Analytics IBM Watson.mp4

08:45

006. 1.5 Real-world Analytics Applications.mp4

13:07

Lesson 2 Introduction to Predictive Analytics and Data Mining

001. Topics.mp4

01:05

002. 2.1 What Is Data Mining, and What Is It Not.mp4

13:53

003. 2.2 The Most Common Data Mining Applications and Tools.mp4

22:28

004. 2.3 Demonstration of Predictive Modeling with Python.mp4

09:45

005. 2.4 Demonstration of Predictive Modeling with KNIME.mp4

09:28

Lesson 3 The Data Mining Process

001. Topics.mp4

01:19

002. 3.1 The Knowledge Discovery in Databases (KDD) Process.mp4

05:57

003. 3.2 Cross-Industry Standard Process for Data Mining (CRISP-DM).mp4

06:57

004. 3.3 Sample, Explore, Modify, Model, and Assess (SEMMA) Process and Six Sigma Process.mp4

12:59

005. 3.4 Demonstration of Data Mining Tools IBM SPSS Modeler and R.mp4

25:04

Lesson 4 Data and Methods in Data Mining

001. Topics.mp4

01:18

002. 4.1 The Nature of Data in Data Mining.mp4

12:37

003. 4.2 Data Mining Methods Predictive versus Descriptive.mp4

09:04

004. 4.3 Evaluation Methods in Data Mining.mp4

13:51

005. 4.4 Classification with Decision Trees.mp4

18:52

006. 4.5 Clustering with the k-means Algorithm.mp4

11:55

007. 4.6 Association Analysis with the Apriori Algorithm.mp4

18:46

Lesson 5 Data Mining Algorithms

001. Topics.mp4

01:06

002. 5.1 Nearest Neighbor Algorithm for Prediction Modeling.mp4

10:13

003. 5.2 Artificial Neural Networks (ANN) and Support Vector Machines (SVM).mp4

20:55

004. 5.3 Linear Regression and Logistic Regression.mp4

14:23

005. 5.4 Demonstration of Linear Regression with Python and KNIME.mp4

11:03

Lesson 6 Text Analytics and Text Mining

001. Topics.mp4

00:54

002. 6.1 Introduction to Text Mining and Natural Language Processing.mp4

05:51

003. 6.2 Text Mining Applications and Text Mining Process.mp4

13:27

004. 6.3 Text Mining Tools and Demonstration of Text Mining Using Rapid Miner.mp4

11:31

005. 6.4 Text Mining Tools and Demonstration of Sentiment Analysis and Topic Modeling with KNIME.mp4

26:24

Lesson 7 Big Data Analytics

001. Topics.mp4

01:03

002. 7.1 What Is Big Data and Where Does It Come From.mp4

07:14

003. 7.2 Fundamental Concepts and Technologies of Big Data.mp4

13:15

004. 7.3 Who Are Data Scientists and Where Do They Come From.mp4

09:29

005. 7.4 Demonstration of Big Data Analytics (SAS Visual Analytics).mp4

16:22

Lesson 8 Predictive Analytics Best Practices

001. Topics.mp4

01:22

002. 8.1 Defining Model Ensembles and Their Pros and Cons.mp4

20:44

003. 8.2 Bias-Variance Tradeoff in Predictive Analytics.mp4

11:26

004. 8.3 Treating the Data-Imbalance Problem with Over- and Undersampling.mp4

13:10

005. 8.4 Explainable MLAIPredictive Analytics.mp4

22:12

006. 8.5 Showcasing Better Practices with a Comprehensive Model of Customer Churn Analysis.mp4

34:12

Summary

001. Predictive Analytics Summary.mp4

03:01

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