Advanced Data Mining Algorithms
( 25 Modules )

Module #1
Introduction to Advanced Data Mining
Overview of data mining, importance of advanced algorithms, and course objectives

Module #2
Review of Fundamental Data Mining Concepts
Review of data mining basics, including supervised and unsupervised learning, clustering, and association rule mining

Module #3
Advanced Clustering Algorithms
In-depth coverage of advanced clustering algorithms, including density-based clustering and hierarchical clustering

Module #4
DBSCAN:Density-Based Clustering
In-depth exploration of the DBSCAN algorithm, including its strengths and weaknesses

Module #5
Hierarchical Clustering:Agglomerative and Divisive Approaches
In-depth exploration of hierarchical clustering, including agglomerative and divisive approaches

Module #6
Anomaly Detection
Introduction to anomaly detection, including techniques and algorithms for identifying outliers

Module #7
One-Class SVM and Local Outlier Factor (LOF)
In-depth exploration of one-class SVM and LOF algorithms for anomaly detection

Module #8
Advanced Association Rule Mining
In-depth coverage of advanced association rule mining techniques, including frequent pattern mining

Module #9
Frequent Pattern Mining:Apriori and Eclat
In-depth exploration of Apriori and Eclat algorithms for frequent pattern mining

Module #10
Recommendation Systems
Introduction to recommendation systems, including content-based and collaborative filtering approaches

Module #11
Content-Based Recommendation Systems
In-depth exploration of content-based recommendation systems, including feature extraction and similarity measures

Module #12
Collaborative Filtering
In-depth exploration of collaborative filtering, including user-based and item-based approaches

Module #13
Dimensionality Reduction
Introduction to dimensionality reduction, including PCA, SVD, and t-SNE

Module #14
Principal Component Analysis (PCA)
In-depth exploration of PCA, including its applications and limitations

Module #15
t-Distributed Stochastic Neighbor Embedding (t-SNE)
In-depth exploration of t-SNE, including its applications and limitations

Module #16
Ensemble Methods
Introduction to ensemble methods, including bagging, boosting, and random forests

Module #17
Gradient Boosting
In-depth exploration of gradient boosting, including its applications and limitations

Module #18
Stacking and Super Learner
In-depth exploration of stacking and super learner ensemble methods

Module #19
Deep Learning for Data Mining
Introduction to deep learning for data mining, including neural networks and autoencoders

Module #20
Convolutional Neural Networks (CNNs) for Data Mining
In-depth exploration of CNNs for data mining, including image and text analysis

Module #21
Recurrent Neural Networks (RNNs) for Data Mining
In-depth exploration of RNNs for data mining, including sequence analysis and forecasting

Module #22
Big Data and Distributed Data Mining
Introduction to big data and distributed data mining, including Hadoop and Spark

Module #23
Distributed Data Mining Algorithms
In-depth exploration of distributed data mining algorithms, including parallel and distributed clustering

Module #24
Data Mining for Big Data
In-depth exploration of data mining for big data, including scalable algorithms and systems

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Advanced Data Mining Algorithms career

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Advanced Data Mining Algorithms ( 25 Modules )

Advanced Data Mining Algorithms
( 25 Modules )