Foundations of Data Science: K-Means Clustering in Python By University of London (Free Online Course) : “Foundations of Data Science: K-Means Clustering in Python” introduces one of the most popular unsupervised machine learning algorithms used to uncover hidden patterns and group data points based on similarities. This course provides a hands-on approach to implementing K-Means clustering using Python, guiding learners from understanding the core concepts to coding real-world applications. K-Means is widely used in customer segmentation, image compression, and anomaly detection. By mastering this technique, learners can enhance their ability to analyze large datasets, identify trends, and make data-driven decisions. Ideal for beginners and intermediate learners, this course bridges theory and practice to build foundational skills in data science.
What You Will Learn?
| Define and explain the key concepts of data clustering |
| Demonstrate understanding of the key constructs and features of the Python language. |
| Implement in Python the principle steps of the K-means algorithm. |
| Design and execute a whole data clustering workflow and interpret the outputs. |
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Concepts Covered in the Course
Week 1: Foundations of Data Science: K-Means Clustering in Python
Week 2: Means and Deviations in Mathematics and Python
Week 3: Moving from One to Two Dimensional Data
Week 4: Introducing Pandas and Using K-Means to Analyse Data
Week 5: A Data Clustering Project
Skills You Will Gain
- K-Means Clustering
- Machine Learning
- Programming in Python
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