Lecture 1 - Data Mining Explained: Decision Lists and Association Rules Made Simple
Lecture 2 - How to Install WEKA: Step-by-Step Guide for Beginners
Lecture 3 - Getting Started with WEKA: A Complete Beginner’s Overview
Lecture 4 - Basics of Bayesian Classification: Understanding Probabilistic Learning
Lecture 5 - A Deep Dive into Bayesian Classification: Concepts and Intuition building
Lecture 6 - Implementing Bayesian Classification Using WEKA
Lecture 7 - Bayesian Classification with Numeric Data: Theory and Examples
Lecture 8 - Decision Trees Explained: From Roots to Leaves
Lecture 9 - Building Decision Trees in WEKA: Hands-On Tutorial
Lecture 10 - Neural Networks Fundamentals: How Artificial Brains Learn
Lecture 11 - Neural Networks Explained: Architecture and Learning
Lecture 12 - Implementing Neural Networks in WEKA
Lecture 13 - Clustering Basics: Grouping Data Without Labels
Lecture 14 - Clustering Algorithms in WEKA: Practical Demonstration
Lecture 15 - Clustering Techniques Explained: Beyond the Basics
Lecture 16 - Principal Component Analysis (PCA): Dimensionality Reduction Explained
Lecture 17 - Applying PCA in WEKA: Step-by-Step Walkthrough
Lecture 18 - Linear Regression Explained: Modeling Relationships in Data
Lecture 19 - Linear Regression Using WEKA: Practical Implementation
Lecture 20 - Logistic Regression Basics: Classification Made Easy
Lecture 21 - Logistic Regression in WEKA: Hands-On Classification
Lecture 22 - Instance-Based Learning Explained: Learning from Examples
Lecture 23 - Instance-Based Learning in WEKA: KNN and Beyond
Lecture 24 - Market Basket Analysis Explained: Discovering Hidden Patterns
Lecture 25 - Market Basket Analysis Using WEKA
Lecture 26 - Linear Discriminant Analysis (LDA): Classification Intuition
Lecture 27 - Linear Discriminant Analysis Explained in Depth
Lecture 28 - Implementing LDA in WEKA
Lecture 29 - Model Evaluation Metrics Explained: Accuracy, Precision and More
Lecture 30 - Support Vector Machines Explained: Maximum Margin Learning
Lecture 31 - Support Vector Machines Deep Dive: Kernels and Optimization
Lecture 32 - Advanced SVM Concepts: Kernels, Margins and Hyperparameters
Lecture 33 - Support Vector Machines in WEKA: Practical Tutorial
