Master the art of unsupervised machine learning with this in-depth course on clustering techniques. Begin by understanding the fundamental concepts of unsupervised learning and how clustering is applied in real-world scenarios. You'll gain insights into key algorithms such as K-Means, hierarchical clustering, and Gaussian Mixture Models, while also learning practical implementation in Python. The course is structured to guide you through various clustering techniques, starting with K-Means clustering. Through a combination of theory, hands-on exercises, and visual walkthroughs, you'll learn how to implement these algorithms, evaluate their effectiveness, and overcome their limitations. Next, you'll dive into hierarchical clustering, exploring its applications in data visualization and real-world contexts, such as evolutionary studies and social media analysis. The final sections cover advanced techniques like Gaussian Mixture Models and Expectation-Maximization, alongside practical comparisons with other methods like K-Means. You'll also explore tools for setting up your environment, coding basics for beginners, and effective learning strategies to optimize your experience in machine learning. Designed for data enthusiasts, analysts, and aspiring machine learning practitioners, this course is ideal for learners with basic Python knowledge who want to deepen their expertise in clustering algorithms. Whether you're a beginner or looking to expand your machine learning toolkit, this course has something for everyone.
Where to get the code
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Skills You'll Learn
Data Preprocessing, K-Means Clustering, Gaussian Mixture Models, Hierarchical Clustering, Python Programming
From the lesson
Getting Set Up
In this module, we will guide you on how to access the course code and supplementary resources. You'll ensure your environment is ready for practical learning and become acquainted with the tools you'll use throughout the course.
Taught By
Packt - Course Instructors