Course Program

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#1Introduction to Data Mining
  • History and Evolution of Data Mining---
  • Applications of Data Mining---
  • Data Mining Process Overview---
#2Understanding Data and Data Formats
  • Types of Data---
  • Data Preprocessing Techniques---
  • Cleaning and Preparing Data---
#3Classification Techniques
  • Decision Trees---
  • Naive Bayes Classifiers---
  • Support Vector Machines---
#4Clustering Techniques
  • K-Means Clustering---
  • Hierarchical Clustering---
  • Density-Based Clustering---
#5Association Rule Learning
  • Apriori Algorithm---
  • Eclat Algorithm---
  • FP-Growth Algorithm---
#6Web Data Mining
  • Web Content Mining---
  • Web Structure Mining---
  • Web Usage Mining---
#7Time Series Analysis
  • Introduction to Time Series---
  • Time Series Forecasting Methods---
  • Real-time Data Analysis---
#8Text Mining
  • Text Preprocessing---
  • Text Classification---
  • Text Clustering Techniques---
#9Big Data and Data Mining
  • Challenges of Big Data---
  • Hadoop Ecosystem---
  • Using Spark for Data Mining---
#10Feature Selection and Extraction
  • Introduction to Feature Selection---
  • Techniques for Feature Extraction---
  • Dimensionality Reduction Methods---
#11Evaluation and Validation
  • Metrics for Model Evaluation---
  • Cross-validation Techniques---
  • Overfitting and Underfitting---
#12Ethics and Privacy in Data Mining
  • Data Privacy Concerns---
  • Ethical Implications---
  • Regulations Governing Data Mining---
#13Practical Applications and Case Studies
  • Analyzing Healthcare Data---
  • Retail and E-commerce---
  • Finance and Fraud Detection Techniques---