Level: Beginner — Author: Writix
Level: Beginner • Duration: 10 hours
Author: Writix
In this course, you will explore the field of unsupervised learning using Python. Imagine a diverse group of customers, each defined by characteristics such as age, location, and financial history. Your goal will be to discover patterns and categorize them into distinct clusters. Alternatively, consider a set of documents, like Wikipedia entries, which you aim to classify based on their content. Unsupervised learning inherently involves uncovering hidden structures within unlabeled data without a specific prediction task. This course will introduce you to various machine learning techniques, including clustering, dimensionality reduction, and matrix factorization. You will gain hands-on experience by implementing key algorithms using libraries like scikit-learn and SciPy. Additionally, you will learn how to cluster, transform, visualize, and extract insights from unlabeled datasets. The course will conclude with a project where you will create a recommender system that suggests popular musical artists.
Duration: 10 hours
XP Points: 350
Participants: 0
- Data enthusiasts looking to improve their skills in machine learning. - Individuals interested in data analysis and pattern recognition. - Python programmers wanting to expand their knowledge in unsupervised learning. - Students and professionals aiming to apply machine learning techniques in real-world scenarios.