Mastering Clustering in Machine Learning | CodeSignal Learn

intermediate

Mastering Clustering in Machine Learning

Machine Learning

4 courses

65 practices

9 hours

Explore unsupervised learning in this path focused on Clustering. Start with data preprocessing, learn algorithms like K-means, DBSCAN, and Hierarchical Clustering, and master validation techniques to evaluate model performance from scratch.

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536 learners

Earn a shareable

Certificate of Achievement

Verified skills you'll gain

INTERMEDIATE

Machine Learning Model Development

INTERMEDIATE

Model Validation and Selection

Tools you'll use

MatPlotLib

Numpy

Pandas

Python

Scikit-learn

SciPy

Seaborn

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Course 1

K-means Clustering Decoded

4 lessons

12 practices

Unlock the secrets of K-means clustering, the backbone of unsupervised learning. You will group data into clusters, identify cluster centroids, and refine cluster quality.

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Course 2

Hierarchical Clustering Deep Dive

4 lessons

18 practices

Unpack the complexity of hierarchical clustering, learning to construct and interpret dendrograms for valuable data insights, and apply your knowledge to real-world data.

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Course 3

Density-Based Clustering Simplified

3 lessons

13 practices

Explore the nuanced world of density-based clustering. Learn to navigate through DBSCAN, focusing on connectivity and density functions to identify unique cluster shapes.

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Course 4

Cluster Performance Unveiled

6 lessons

22 practices

Explore an in-depth analysis of clustering model validation, delving into techniques that evaluate, refine, and optimize the performance of clustering algorithms. We'll discuss the Silhouette Score, Davis-Bouldin Index, and Cross-Tabulation Analysis, learning how to implement these practices to identify optimal clustering structures.

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