ML Algorithms: Clustering

Explore unsupervised learning with our "Machine Learning Algorithms: Clustering" course. This course is designed to provide a comprehensive understanding of clustering techniques and their applications in the real world. By the end, you'll be equipped to implement and evaluate various clustering models to group data effectively and uncover hidden patterns.

DIFFICULTY

Beginner to Intermediate

COURSE TYPE

SCHEDULE

Self-paced

PRE-REQUISITES

Basic knowledge of Python and elementary statistics recommended

What you'll learn

Introduction to
Machine Learning

Understand the basics of machine learning, including its types and real-world applications.

Data
Handling

Learn about data types, preprocessing techniques, and exploratory data analysis.

Clustering
Models

Dive deep into partition-based clustering, hierarchical clustering, density-based clustering, and grid-based clustering techniques.

Model
Evaluation

Understand how to evaluate models using metrics like Within-Cluster Sum of Squares (WCSS), Silhouette Score, and more.

Advanced
Techniques

Explore advanced clustering techniques like K-Means, DBSCAN, and Hierarchical Clustering.

Handling
Data

Learn techniques to manage different types of data for better model performance.

K-Means
Clustering Models

Develop models to partition data into distinct clusters.

Hierarchical
Clustering Models

Learn to create tree-like structures to group data points.

DBSCAN
Clustering Models

Implement density-based clustering techniques to identify core, border, and noise points.

Real-World
Projects

Apply your knowledge to datasets like customer segmentation and urban planning to predict outcomes and understand data relationships.

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Frequently Asked Questions

What types of clustering models will I learn in this course?

You will learn partition-based clustering, hierarchical clustering, density-based clustering, and advanced techniques like K-Means, DBSCAN, and Hierarchical Clustering.

Will this course cover data preprocessing techniques?

Yes, the course covers data preprocessing techniques such as handling missing values, encoding categorical variables, and normalizing data.

Does the course include practical coding exercises?

Yes, the course includes practical coding exercises to help you apply clustering techniques to real-world datasets.

What kind of projects will I work on in this course?

You will work on projects like customer segmentation, urban planning for park development, and identifying popular gathering spots based on social media check-ins.

Do I need prior knowledge of machine learning to take this course?

Basic knowledge of Python and statistics is recommended, but not mandatory. The course is designed to be accessible to learners with varying levels of experience.