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Last updated:

September 25, 2023

Duration:

Unlimited Duration

FREE

This course includes:

Unlimited Duration

Badge on Completion

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Unlimited Duration

Description

Data Mining. Instructor: Prof. Pabitra Mitra, Department of Computer Science and Engineering, IIT Kharagpur. Data mining is study of algorithms for finding patterns in large data sets

It is an integral part of modern industry, where data from its operations and customers are mined for gaining business insight. It is also important in modern scientific endeavors. Data mining is an interdisciplinary topic involving, databases, machine learning and algorithms. The course will cover the fundamentals of data mining. It will explain the basic algorithms like data preprocessing, association rules, classification, clustering, sequence mining and visualization. It will also explain implementations in open source software. Finally, case studies on industrial problems will be demonstrated. (from nptel.ac.in)

Course Curriculum

  • Lecture 01 – Introduction, Knowledge Discovery Process Unlimited
  • Lecture 02 – Data Processing: Data Types and Attributes Unlimited
  • Lecture 03 – Data Processing: Data Quality, Processing Steps Unlimited
  • Lecture 04 – Association Rules Unlimited
  • Lecture 05 – Association Rule: Apriori Algorithm Unlimited
  • Lecture 06 – Apriori Algorithm: Rule Generation Unlimited
  • Lecture 07 – Classification, Supervised Learning Unlimited
  • Lecture 08 – Decision Tree Inference Unlimited
  • Lecture 09 – Decision Tree Construction Unlimited
  • Lecture 10 – Decision Tree Construction Unlimited
  • Lecture 11 – Decision Tree Pruning and Extensions Unlimited
  • Lecture 12 – Bayes Classifier: Class Conditional Probabilities Unlimited
  • Lecture 13 – Bayes Classifier: Posterior Probability, MAP Unlimited
  • Lecture 14 – Bayes Classifier: Multivariate Bayes Unlimited
  • Lecture 15 – Bayes Classifier: Naive Bayes Unlimited
  • Lecture 16 – Bayes Classifier: Conditional Independence Unlimited
  • Lecture 17 – K-Nearest Neighbor Classifiers Unlimited
  • Lecture 18 – K-Nearest Neighbor: Distance Function, Choice of K Unlimited
  • Lecture 19 – K-Nearest Neighbor Classification Techniques Unlimited
  • Lecture 20 – K-Nearest Neighbor: High Dimensional Search Unlimited
  • Lecture 21 – K-Nearest Neighbor: Classifier Evaluation Unlimited
  • Lecture 22 – Support Vector Machine: Linear Discriminant Unlimited
  • Lecture 23 – Support Vector Machine: Separating Hyperplane Unlimited
  • Lecture 24 – Support Vector Machine: Maximum Margin Hyperplane Unlimited
  • Lecture 25 – Support Vector Machine: Dual Optimization Problem Unlimited
  • Lecture 25 – Support Vector Machine: Dual Optimization Problem Unlimited
  • Lecture 26 – Support Vector Machine: Support Vectors Unlimited
  • Lecture 27 – Kernel Machines: Soft Margin Hyperplane, Kernels Unlimited
  • Lecture 28 – Artificial Neural Networks: Perceptron Unlimited
  • Lecture 29 – Artificial Neural Networks: Learning Rule Unlimited
  • Lecture 30 – Artificial Neural Networks: Multilayer Perceptron Unlimited
  • Lecture 31 – Artificial Neural Networks: Backpropagation Unlimited
  • Lecture 32 – Basics of Clustering Unlimited
  • Lecture 33 – Hierarchical Clustering Unlimited
  • Lecture 34 – Clustering: K-Means Unlimited
  • Lecture 35 – Clustering: DBSCAN Unlimited
  • Lecture 36 – Clustering: Evaluation Unlimited
  • Lecture 37 – Regression Problem Unlimited
  • Lecture 38 – Linear Regression Unlimited
  • Lecture 39 – Nonlinear Regression Unlimited
  • Lecture 40 – Regression: Overfitting Unlimited
  • Lecture 41 – Dimensionality Reduction: Feature Selection Unlimited
  • Lecture 42 – Dimensionality Reduction: Principal Component Analysis Unlimited
  • Lecture 43 – Tutorial Unlimited

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