Introduction to Machine Learning: Classification and Clustering Models

This workshop offers an exploration of machine learning models for clustering and classification. With the increasing availability of large datasets, these models play a crucial role in extracting valuable insights and making informed decisions. In this workshop, participants will gain insight into clustering algorithms such as K-means, explore popular classification algorithms like decision trees, and learn about anomaly detection. Through a combination of lectures and hands-on exercises, participants will learn how to preprocess data, select relevant features, and evaluate model performance. By the conclusion of the workshop, participants will have a solid foundation in building and deploying machine learning models for clustering and classification tasks.

In this workshop, we will use cloud-based platforms, so you don’t need to have Python installed. Please make sure that you have a Google Colaboratory (https://colab.research.google.com/) account. This workshop will involve hands-on exercises that require the use of programming tools and libraries commonly used in machine learning, such as Python and Scikit-learn. As such, prior familiarity with Python programming is recommended for participants to fully benefit from the practical component of the workshop.

Learning objectives

At the end of this workshop, you will be able to:

1. Understand classification & clustering models, and their applications in machine learning.
2. Train classification and clustering models for real-world datasets.
3. Interpret and analyze classification and clustering model results.

Schedule

Slides

Find the workshop slides below or open it in a new tab:

Other workshops in the series

Workshop 1- Regression: Click here
Workshop 3- Neural Networks: Click here

Resources

• Colab Notebook:

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