Building Clustering Models with scikit-learn
This course covers several important techniques used to implement clustering in scikit-learn, including the K-means, mean-shift and DBScan clustering algorithms, as well as the role of hyperparameter tuning, and performing clustering on image data.
What you'll learn
Clustering is an extremely powerful and versatile unsupervised machine learning technique that is especially useful as a precursor to applying supervised learning techniques like classification. In this course, Building Clustering Models with scikit-learn, you will gain the ability to enumerate the different types of clustering algorithms and correctly implement them in scikit-learn. First, you will learn what clustering seeks to achieve, and how the ubiquitous k-means clustering algorithm works under the hood. Next, you will discover how to implement other techniques such as DBScan, mean-shift, and agglomerative clustering. You will then understand the importance of hyperparameter tuning in clustering, such as identifying the correct number of clusters into which your data ought to be partitioned. Finally, you will round out the course by implementing clustering algorithms on image data - an especially common use-case. When you are finished with this course, you will have the skills and knowledge to select the correct clustering algorithm based on the problem you are trying to solve, and also implement it correctly using scikit-learn.
Table of contents
- Version Check 0m
- Module Overview 1m
- Prerequisites and Course Outline 2m
- Supervised and Unsupervised Learning 5m
- Clustering Objectives and Use Cases 9m
- K-means Clustering 4m
- Evaluating Clustering Models 6m
- Getting Started with scikit-learn Install and Setup 3m
- Performing K-means Clustering 6m
- Evaluating K-means Clustering 8m
- Exploring the Iris Dataset 4m
- Performing K-means Clustering and Evaluation 6m
- Module Overview 1m
- Categories of Clustering Algorithms 4m
- Setting up Helper Functions to Perform Clustering 3m
- Choosing Clustering Algorithms 7m
- Hierarchical Clustering 5m
- Agglomerative Clustering 5m
- DBSCAN Clustering 5m
- Mean-shift Clustering 7m
- BIRCH Clustering 3m
- Affinilty Propagation Clustering 4m
- Mini-batch K-means Clustering 3m
- Spectral Clustering Using a Precomputed Matrix 8m
- Module Overview 1m
- Understanding the Silhouette Score 3m
- K-means Number of Clusters: The Elbow Method 5m
- K-means Number of Clusters: The Silhouette Method 4m
- Seeds and Distance Measures 2m
- Hyperparameter Tuning: K-means Clustering 6m
- Hyperparameter Tuning: DBSCAN Clustering 6m
- Hyperparameter Tuning: Mean-shift Clustering 2m
About the author
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing ... more