Introduction:

Unsupervised Learning is a powerful branch of machine learning where the algorithm is trained on unlabeled data to uncover hidden patterns, structures, and relationships. This 5-day course will provide participants with a comprehensive understanding of unsupervised learning techniques and their applications across various domains, such as customer segmentation, anomaly detection, and data compression. Participants will explore algorithms such as clustering, dimensionality reduction, and autoencoders, using real-world datasets to solve complex problems. By the end of the course, attendees will be able to apply unsupervised learning methods to a wide range of practical scenarios.

Objectives:

By the end of this course, participants will:

• Understand the fundamental principles and techniques in unsupervised learning.
• Learn how to apply clustering algorithms such as K-means, DBSCAN, and hierarchical clustering.
• Gain hands-on experience with dimensionality reduction techniques such as PCA and t-SNE.
• Explore advanced unsupervised learning models like autoencoders and GANs (Generative Adversarial Networks).
• Learn how to evaluate and interpret the results of unsupervised learning models.
• Be prepared to use unsupervised learning techniques to solve real-world data problems, including anomaly detection and feature extraction.

Who Should Attend:

This course is ideal for:

• Data Scientists, Machine Learning Engineers, and AI Researchers who want to specialize in unsupervised learning techniques.
• Professionals working in fields such as finance, healthcare, and marketing who want to apply unsupervised learning methods to analyze and understand large datasets.
• Software engineers and developers looking to integrate unsupervised learning into real-world applications like fraud detection, customer segmentation, and recommendation systems.
• Researchers and students with a strong foundation in machine learning, eager to explore unsupervised learning for complex data problems.

Day 1: Introduction to Unsupervised Learning

• Morning:
  • What is Unsupervised Learning?:
    • Overview of unsupervised learning and its role in machine learning.
    • Differences between supervised and unsupervised learning.
    • Key applications of unsupervised learning: clustering, anomaly detection, and dimensionality reduction.
  • Understanding the Unlabeled Data:
    • How to handle and preprocess unlabeled data.
    • Importance of data normalization and scaling in unsupervised learning.
• Afternoon:
  • Types of Unsupervised Learning Algorithms:
    • Overview of clustering, dimensionality reduction, and density estimation.
    • Introduction to common unsupervised learning methods: K-means, DBSCAN, PCA, and autoencoders.
  • Hands-on Session:
    • Implementing K-means clustering on a sample dataset to segment data points into clusters.

Day 2: Clustering Algorithms

• Morning:
  • Introduction to Clustering:
    • What is clustering? Types of clustering algorithms: hard vs. soft clustering.
    • K-means clustering: algorithm steps, centroid initialization, and convergence.
  • Advanced Clustering Algorithms:
    • DBSCAN (Density-Based Spatial Clustering of Applications with Noise).
    • Agglomerative hierarchical clustering and its use in hierarchical structure discovery.
• Afternoon:
  • Evaluating Clustering Results:
    • Understanding cluster evaluation metrics: silhouette score, Davies-Bouldin index, and cluster purity.
    • Visualizing clusters using 2D and 3D plots.
  • Hands-on Session:
    • Implementing DBSCAN and hierarchical clustering on a sample dataset.
    • Evaluating and comparing the results using cluster evaluation metrics.

Day 3: Dimensionality Reduction Techniques

• Morning:
  • Introduction to Dimensionality Reduction:
    • Why dimensionality reduction is important for high-dimensional data.
    • Curse of dimensionality and its impact on machine learning models.
  • Principal Component Analysis (PCA):
    • How PCA works: eigenvectors, eigenvalues, and dimensionality reduction through projection.
    • Interpreting PCA components and selecting the optimal number of components.
• Afternoon:
  • t-SNE (t-Distributed Stochastic Neighbor Embedding):
    • Introduction to t-SNE for non-linear dimensionality reduction.
    • How t-SNE differs from PCA and its advantages in visualizing high-dimensional data.
  • Hands-on Session:
    • Using PCA and t-SNE for visualizing and reducing the dimensions of high-dimensional data (e.g., image data or text data).

Day 4: Autoencoders and Advanced Models

• Morning:
  • Introduction to Autoencoders:
    • What are autoencoders? Structure and working principles of autoencoders (encoder-decoder architecture).
    • Applications of autoencoders: anomaly detection, data compression, and denoising.
  • Types of Autoencoders:
    • Variational Autoencoders (VAE) for generative tasks.
    • Sparse and contractive autoencoders for feature learning and denoising.
• Afternoon:
  • Generative Adversarial Networks (GANs):
    • Understanding GANs: generator vs. discriminator.
    • Applications of GANs in data generation, image synthesis, and domain adaptation.
  • Hands-on Session:
    • Implementing a basic autoencoder for anomaly detection in sensor data or images.
    • Training and visualizing the results of a VAE or GAN on a dataset.

Day 5: Real-World Applications and Case Studies

• Morning:
  • Anomaly Detection with Unsupervised Learning:
    • Using clustering and autoencoders for detecting outliers and anomalies in data.
    • Applications in fraud detection, network security, and predictive maintenance.
  • Unsupervised Learning for Feature Extraction:
    • How unsupervised learning can be used to automatically extract relevant features for downstream tasks (e.g., classification, regression).
• Afternoon:
  • Real-World Case Studies:
    • Customer segmentation in marketing using clustering.
    • Dimensionality reduction for image processing and speech recognition.
    • Anomaly detection in financial transactions using unsupervised learning.
  • Final Hands-On Project:
    • Applying unsupervised learning techniques (e.g., clustering, PCA, autoencoders) to solve a real-world problem of the participant’s choice (e.g., customer segmentation, anomaly detection).
  • Wrap-Up and Future Trends:
    • Future directions in unsupervised learning: deep learning-based unsupervised methods, self-supervised learning, and unsupervised learning in reinforcement learning.
    • Discussion of resources for further learning and research.

Key Takeaways:

• Deep understanding of the key unsupervised learning algorithms and their applications.
• Practical experience with clustering, dimensionality reduction, and autoencoders.
• Ability to apply unsupervised learning techniques to solve real-world data analysis challenges.
• Exposure to advanced models like GANs and VAEs, and their potential in generative tasks.
• Knowledge of the latest trends and challenges in unsupervised learning, including future research areas.