Unsupervised Learning Models

Supervised Learning : Clustering Models:

1. K-Means Clustering
2. K-Medoids (PAM - Partition Around Medoids)
3. Hierarchical Clustering
4. Density-Based Clustering
• DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Handles noise and clusters with arbitrary shapes.
• OPTICS (Ordering Points to Identify Clustering Structure): Extends DBSCAN to detect clusters of varying densities.
5. Model-Based Clustering
• Gaussian Mixture Models (GMM): Probabilistic model assuming data is generated from a mixture of Gaussian distributions.
• Expectation-Maximization (EM): Iterative approach to optimize Gaussian Mixture Models.

1. Principal Component Analysis (PCA)
2. Kernel PCA
3. t-Distributed Stochastic Neighbor Embedding (t-SNE)
4. Uniform Manifold Approximation and Projection (UMAP)
5. Factor Analysis
6. Independent Component Analysis (ICA)

Association Rule Mining (For finding relationships between items in large datasets)

1. Apriori Algorithm
• Identifies frequent itemsets and generates association rules.
• Example: "If a customer buys bread, they are likely to buy butter."
2. Eclat Algorithm
• Efficient alternative to Apriori using depth-first search for frequent itemsets.
3. FP-Growth (Frequent Pattern Growth)
• Creates a compressed representation of the dataset for efficient mining of frequent patterns.

Anomaly Detection Models (For identifying rare or abnormal data points)

1. Isolation Forest
• Randomly splits data to isolate anomalies efficiently.
2. One-Class SVM
• SVM tailored for identifying outliers in high-dimensional spaces.
3. Elliptic Envelope
• Fits a multivariate Gaussian distribution to detect outliers.
4. Auto-encoders
• Neural network-based method for reconstructing data; anomalies are identified by high reconstruction error.

Latent Variable Models (For discovering hidden structures in data)

1. Latent Dirichlet Allocation (LDA)
• Topic modeling in text data, identifying topics as latent variables.
2. Restricted Boltzmann Machines (RBM)
• Used for feature learning and collaborative filtering.
3. Autoencoders (Dimensionality Reduction and Anomaly Detection)
• Encodes data into a compressed representation and reconstructs it.

Reinforcement Models (Unsupervised learning for exploratory use)

1. Self-Organizing Maps (SOM)
• Neural network model for mapping high-dimensional data into 2D grids.
2. Generative Models (Also used in unsupervised learning tasks)
• Variational Autoencoders (VAEs): Learn latent space representations of data.
• Generative Adversarial Networks (GANs): Learn to generate new data points similar to the original dataset.

I want to learn everything about [MODEL NAME] as a data scientist. Include both theory and practical aspects. Give me a complete module-style course structure with sections on:
1. Basic concept and use cases
2. Theoretical foundations and math (if any)
3. Parameters and tuning
4. Preprocessing requirements
5. Implementation from scratch (optional)
6. Implementation using Scikit-learn (or other libraries)
7. Evaluation techniques and metrics
8. Pros and cons
9. Best practices and common pitfalls
10. Real-world projects or case studies
11. Comparison with other models
12. Additional resources (articles, videos, papers)
Structure the content in modules for easy self-paced learning.