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Predictive Analytics and Data Mining
Intermediate
Online
4 Weeks
Free Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 4490 | yes | Coursera |
The Syllabus
Videos
- Welcome To Predictive Analytics And Data Mining
- Meet Professor Sridhar Seshadri
- Rattle Installation Guidelines For Windows
- R And Rattle Installation Instructions For Mac Os
- Overview Of Rattle
- Lecture 1-1: Introduction To Clustering
- Lecture 1-2: Applications Of Clustering
- Lecture 1-3: How To Cluster
- Lecture 1-4: Introduction To K Means
- Lecture 1-5: Hierarchical (Agglomerative) Clustering
- Lecture 1-6: Measuring Similarity Between Clusters
- Lecture 1-7: Real World Clustering Example
- Lecture 1-8: Clustering Practice And Summary
Readings
- Syllabus
- About The Discussion Forums
- Glossary
- Brand Descriptions
- Update Your Profile
- Module 0 Agenda
- Rattle Tutorials (Interface, Windows, Mac)
- Frequent Asked Questions
- Module 1 Overview
- Module 1 Readings, Data Sets, And Slides
- Module 1 Peer Review Assignment Answer Key
Practice Exercises
- Orientation Quiz
- Module 1 Practice Problems
- Module 1 Graded Quiz
Videos
- Lecture 2-1: Introduction To Discriminative Classifiers
- Lecture 2-2: Model Complexity
- Lecture 2-3: Rule Based Classifiers
- Lecture 2-4: Entropy And Decision Trees
- Lecture 2-5: Classification Tree Example
- Lecture 2-6: Regression Tree Example
- Lecture 2-7: Introduction To Forests And Spam Filter Exercise
Readings
- Module 2 Overview
- Module 2 Readings, Data Sets, And Slides
- Module 2 Peer Review Assignment Answer Key
Practice Exercises
- Module 2 Practice Problems
- Module 2 Graded Quiz
Videos
- Lecture 3-1: Introduction To Rules
- Lecture 3-2: K-Nearest Neighbor
- Lecture 3-3: K-Nearest Neighbor Classifier
- Lecture 3-4: Selecting The Best K In Rstudio
- Lecture 3-5: Bayes' Rule
- Lecture 3-6: The Naïve Bayes Trick
- Lecture 3-7: Employee Attrition Example
- Lecture 3-8: Employee Attrition Example In Rstudio, Exercise, And Summary
Readings
- Module 3 Overview
- Module 3 Readings, Data Sets, And Slides
- Module 3 Peer Review Assignment Answer Key
Practice Exercises
- Module 3 Practice Problems
- Module 3 Graded Quiz
Videos
- Lecture 4-1: Introduction To Model Performance
- Lecture 4-2: Classification Tree Example
- Lecture 4-3: True And False Negatives
- Lecture 4-4: Clock Example Exercise
- Lecture 4-5: Making Recommendations
- Lecture 4-6: Association Rule Mining
- Lecture 4-7: Collaborative Filtering
- Lecture 4-8: Recommendation Example In Rstudio And Summary
Readings
- Module 4 Overview
- Module 4 Readings, Data Sets, And Slides
- Module 4 Peer Review Assignment Answer Key
Practice Exercises
- Module 4 Practice Problems
- Module 4 Graded Quiz
Instructors
Articles
14 Jul'25, 06:49 AM
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6 Oct'22, 04:53 AM
2 Mar'22, 08:24 AM
14 Jun'21, 09:46 AM
14 Jul'25, 06:49 AM
5 Apr'24, 04:40 AM
9 Mar'24, 07:07 AM
3 Apr'23, 05:01 AM
6 Oct'22, 04:53 AM
2 Mar'22, 08:24 AM
14 Jun'21, 09:46 AM