Clustering and Unsupervised Learning Techniques

Lesson 23/41 | Study Time: 20 Min

Course: AI-ML Foundations

Clustering is a technique used to group similar data points together. It is part of unsupervised learning because it does not require labeled data.

K-Means clustering is one of the most popular algorithms. It divides data into clusters based on similarity. Each cluster has a centroid, and data points are assigned to the nearest centroid.

Clustering is widely used in marketing for customer segmentation. Businesses group customers based on behavior to create targeted marketing strategies.

Another application is recommendation systems, where similar users or items are grouped together.

Clustering also helps in anomaly detection by identifying data points that do not fit into any cluster.

One challenge is determining the number of clusters. Techniques like the elbow method help in selecting the optimal number.

Clustering is powerful for discovering hidden patterns and insights in data.

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Arjun Mehta

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1- Introduction to Data Management in AI/ML 2- Overview of data sources 3- Methods for Acquiring Data 4- Importance of Data Cleaning and Preprocessing 5- Hear from an Expert: The Value of Consistent Taxonomy 6- Introduction to RAG 7- Best Practices for Maintaining Efficient Data Sources for RAG 8- Hear from an Expert: Security Considerations When Working with Data 9- Summary: Data Management in AI/ML 10- Hear from an Expert: Industry Exemplar 11- Walkthrough: Setting up your environment in Microsoft Azure (Optional) 12- Selecting the right model deployment strategy in Microsoft Azure 13- Walkthrough: Justifying your choice of model selection (Optional) 14- Introduction to Machine Learning Models 15- Course syllabus: Foundations of AI and Machine Learning Infrastructure 16- The structure and role of data sources and pipelines explained 17- Supervised vs Unsupervised Learning Models 18- In-depth exploration of data sources and pipelines 19- Understanding Regression Models in Detail 20- Model development frameworks and their applications explained 21- Key considerations in selecting a model development framework 22- Understanding Classification Models in Detail 23- Clustering and Unsupervised Learning Techniques 24- Model Selection Strategies 25- Introduction to Scikit-learn 26- Introduction to TensorFlow and PyTorch 27- Model Training and Validation 28- Evaluating and Comparing Machine Learning Models 29- Introduction to Considerations when deploying platforms 30- Best Practices for Packaging and Containerizing Models 31- Tools and Frameworks for Model Deployment 32- Instructions: Preparing a Model for Deployment 33- Tools and Practices for Version Control (Git, DVC) 34- Implementing Version Control for Reproducibility 35- End-to-End Machine Learning Project Workflow 36- Case Study: Building a Recommendation System 37- Case Study: Spam Detection System 38- Real-World Challenges in Machine Learning 39- Criteria for Evaluating Deployment Platforms 40- Capstone Project: Build Your Own ML Solution 41- Real-world Case Studies of Successful AI/ML Deployments