Data Mining Algorithms and Applications
( 30 Modules )

Module #1
Introduction to Data Mining
Overview of data mining, its importance, and applications

Module #2
Data Preprocessing
Handling missing values, data transformation, and data normalization

Module #3
Data Visualization
Introduction to data visualization, types of plots, and visualization tools

Module #4
Supervised Learning - Introduction
Overview of supervised learning, types of supervised learning, and evaluation metrics

Module #5
Decision Trees
Introduction to decision trees, construction, and advantages

Module #6
Random Forests
Introduction to random forests, ensemble learning, and hyperparameter tuning

Module #7
Support Vector Machines (SVMs)
Introduction to SVMs, kernel functions, and SVM algorithms

Module #8
K-Nearest Neighbors (KNN)
Introduction to KNN, distance metrics, and k-value selection

Module #9
Naive Bayes
Introduction to Naive Bayes, Bayes theorem, and classification

Module #10
Unsupervised Learning - Introduction
Overview of unsupervised learning, types of unsupervised learning, and evaluation metrics

Module #11
K-Means Clustering
Introduction to K-means clustering, algorithm, and applications

Module #12
Hierarchical Clustering
Introduction to hierarchical clustering, agglomerative and divisive clustering

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

Module #14
Association Rule Mining
Introduction to association rule mining, Apriori algorithm, and applications

Module #15
Text Mining
Introduction to text mining, text preprocessing, and text classification

Module #16
Recommendation Systems
Introduction to recommendation systems, collaborative filtering, and content-based filtering

Module #17
Deep Learning for Data Mining
Introduction to deep learning, neural networks, and deep learning for data mining

Module #18
Big Data and Data Mining
Challenges of big data, Hadoop, and Spark for data mining

Module #19
Data Mining Applications in Healthcare
Applications of data mining in healthcare, disease prediction, and patient profiling

Module #20
Data Mining Applications in Finance
Applications of data mining in finance, credit risk analysis, and portfolio optimization

Module #21
Data Mining Applications in Marketing
Applications of data mining in marketing, customer segmentation, and churn prediction

Module #22
Data Mining Applications in Social Media
Applications of data mining in social media, sentiment analysis, and influencer identification

Module #23
Data Mining Ethics and Privacy
Ethical considerations in data mining, privacy concerns, and GDPR compliance

Module #24
Data Mining Tools and Software
Overview of data mining tools and software, including R, Python, and Excel

Module #25
Case Studies in Data Mining
Real-world case studies in data mining, best practices, and lessons learned

Module #26
Data Mining Project Development
Developing a data mining project, problem definition, and project planning

Module #27
Data Mining Model Evaluation
Evaluating data mining models, metrics, and model selection

Module #28
Data Mining Model Deployment
Deploying data mining models, model integration, and model maintenance

Module #29
Data Mining Best Practices
Best practices in data mining, common pitfalls, and lessons learned

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Data Mining Algorithms and Applications career

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Data Mining Algorithms and Applications ( 30 Modules )

Data Mining Algorithms and Applications
( 30 Modules )