Advanced Methods in Data Mining and Analytics
( 25 Modules )

Module #1
Introduction to Advanced Data Mining and Analytics
Overview of advanced data mining and analytics techniques, importance, and applications

Module #2
Supervised Learning:Advanced Regression Techniques
Discussion of advanced regression techniques, including regularization, Lasso, Ridge, and Elastic Net

Module #3
Supervised Learning:Advanced Classification Techniques
Discussion of advanced classification techniques, including SVM, KNN, and Random Forest

Module #4
Unsupervised Learning:Clustering Algorithms
Discussion of clustering algorithms, including K-Means, Hierarchical Clustering, and DBSCAN

Module #5
Unsupervised Learning:Dimensionality Reduction Techniques
Discussion of dimensionality reduction techniques, including PCA, t-SNE, and Autoencoders

Module #6
Ensemble Methods
Discussion of ensemble methods, including Bagging, Boosting, and Stacking

Module #7
Advanced Feature Engineering Techniques
Discussion of advanced feature engineering techniques, including feature selection and extraction

Module #8
Handling Imbalanced Datasets
Discussion of techniques for handling imbalanced datasets, including oversampling, undersampling, and SMOTE

Module #9
Introduction to Deep Learning
Overview of deep learning concepts, including neural networks and optimizers

Module #10
Deep Learning for Computer Vision
Discussion of deep learning techniques for computer vision, including CNNs and Transfer Learning

Module #11
Deep Learning for Natural Language Processing
Discussion of deep learning techniques for NLP, including RNNs, LSTMs, and Word Embeddings

Module #12
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks
In-depth discussion of RNNs and LSTMs, including architecture and applications

Module #13
Advanced Natural Language Processing Techniques
Discussion of advanced NLP techniques, including topic modeling and sentiment analysis

Module #14
Graph-Based Methods
Discussion of graph-based methods, including graph neural networks and network analysis

Module #15
Recommendation Systems
Discussion of recommendation systems, including collaborative filtering and content-based filtering

Module #16
Big Data and Distributed Computing
Discussion of big data processing using distributed computing frameworks, including Hadoop and Spark

Module #17
Streaming Data and Real-Time Analytics
Discussion of streaming data and real-time analytics, including Kafka and Spark Streaming

Module #18
Anomaly Detection and Outlier Analysis
Discussion of anomaly detection and outlier analysis techniques, including statistical and machine learning methods

Module #19
Time Series Analysis and Forecasting
Discussion of time series analysis and forecasting techniques, including ARIMA, Prophet, and LSTM

Module #20
Advanced Visualization Techniques
Discussion of advanced visualization techniques, including interactive visualizations and geospatial analysis

Module #21
Explainability and Interpretability in Machine Learning
Discussion of explainability and interpretability techniques in machine learning, including LIME and SHAP

Module #22
Fairness and Bias in Machine Learning
Discussion of fairness and bias in machine learning, including techniques for detecting and mitigating bias

Module #23
Case Studies in Advanced Data Mining and Analytics
Real-world case studies of advanced data mining and analytics applications, including healthcare, finance, and marketing

Module #24
Ethics in Data Mining and Analytics
Discussion of ethical considerations in data mining and analytics, including privacy, security, and transparency

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

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Advanced Methods in Data Mining and Analytics ( 25 Modules )

Advanced Methods in Data Mining and Analytics
( 25 Modules )