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May 10, 2024
What you'll learn
- Introduction to Data Mining
- Understanding the fundamentals of data mining
- Overview of data mining techniques and applications
- Data Preprocessing and Cleaning
- Techniques for handling missing data and outliers
- Data normalization and transformation
- Supervised Learning for Classification
- Introduction to classification algorithms (Decision Trees, SVM, etc.)
- Building and evaluating classification models
- Unsupervised Learning for Clustering
- Exploring clustering algorithms (K-Means, Hierarchical clustering, etc.)
- Practical implementation of clustering techniques
- Association Rule Mining
- Understanding association rule mining and its applications
- Apriori algorithm for discovering association rules
- Project and internship
