Data Mining Concepts and Techniques

Course Features

Course Details

Module 1. About the Course
Module 2. Introduction to Data Mining
Overview
Module Overview
What is Data Mining
Statistics in Data Mining
Machine Learning
Supervised Learning
Unsupervised Learning
Summary
Module 3. The Data Mining Process (24 min) 
Module Overview
Data Mining Framework
Data Mining Approaches
Data Mining Techniques
Data Mining Process
Summary
Module 4. Exploratory Data Analysis
Overview
Exploratory Data Analysis
Data Profiling: Uncovering Structure
Data Profiling: Types of Profiling
Descriptive Statistics
Results of Data Profiling and Descriptive Statistics
Data Relationships
Findings – Important Variables
Visualization Techniques
Outcomes and Interpretations
Sampling Size
Sample Quality
Big Data Considerations
Feature Selection
EDA Checklist
Summary
Module 5. Data Mining Models and Algorithms
Overview
Build the Model
Anatomy of a Model
What is a Classification Problem
Classification
Ensemble Methods
Clustering
Clustering Uses
Association−Market Basket
Association Uses
Application of Data Mining Models
Model Selection
Summary
Module 6. Model Validation Techniques
Overview
Module Overview
The Validation Process
Fitting a Model
Bias/Variance Tradeoff
Regression – Mean Squared Error
Linear Regression – Confidence and Prediction Intervals
Logistic Regression – Significance Test
Classification Accuracy
Classification Accuracy – Other Measures
Prediction Error Methods
Hold-Out Cross Validation
K-Fold Cross Validation Method
Summary
Module 7. Deploying Data Mining Tools 
Overview
Deploying Data Mining Models
Course Summary
This course does not have any sections.

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