Data Mining & Machine Learning

Reference Material

Comprehensive exploration of data mining techniques, machine learning algorithms, pattern recognition, neural networks, and ethical AI practices. From data representation through intelligent systems.

Course Materials

Overview

Introduction

Foundational concepts of data mining and machine learning, their relationship, applications in modern AI systems, and the complete machine learning pipeline.

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Fundamentals

Data Representation & Analysis

Comprehensive study of data representation techniques, manipulation methods, feature engineering, preprocessing pipelines, and exploratory data analysis for machine learning.

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Core Techniques

Patterns, Tasks & Algorithms

Pattern recognition, data mining tasks including classification, clustering, regression, decision trees, k-means, SVM, and ensemble methods.

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Advanced AI

Neural Networks & Ethics

Artificial neural networks, deep learning architectures, backpropagation, ethical considerations in AI deployment, bias detection, and responsible machine learning practices.

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Semantic Web

Linked Open Data

Semantic web technologies, RDF frameworks, SPARQL queries, knowledge graphs for machine learning, and integrating linked data with ML pipelines.

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Practical Sessions

GitHub Repository

DataMining - Practical Exercises

Comprehensive collection of hands-on projects covering data mining algorithms, machine learning implementations, neural network training, and real-world dataset analysis.

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