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    Quick Facts

    Medium Of InstructionsMode Of LearningMode Of Delivery
    EnglishSelf StudyVideo and Text Based

    Courses and Certificate Fees

    Fees InformationsCertificate AvailabilityCertificate Providing Authority
    INR 4490yesCoursera

    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

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