{"id":5589,"date":"2026-08-10T09:24:26","date_gmt":"2026-08-10T09:24:26","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-python-data-science-unsupervised-machine-learning-2024-11\/"},"modified":"2026-08-10T09:24:26","modified_gmt":"2026-08-10T09:24:26","slug":"udemy-python-data-science-unsupervised-machine-learning-2024-11","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-python-data-science-unsupervised-machine-learning-2024-11\/","title":{"rendered":"Udemy \u2013 Python Data Science: Unsupervised Machine Learning 2024-11"},"content":{"rendered":"
\n

Descriptions<\/h2>\n

Python Data Science: Unsupervised Machine Learning, This is a hands-on, project-based course designed to help you master the foundations for unsupervised machine learning in Python. We\u2019ll start by reviewing the Python data science workflow, discussing the techniques & applications of unsupervised learning, and walking through the data prep steps required for modeling. You\u2019ll learn how to set the correct row granularity for modeling, apply feature engineering techniques, select relevant features, and scale your data using normalization and standardization. From there we\u2019ll fit, tune, and interpret 3 popular clustering models using scikit-learn. We\u2019ll start with K-Means Clustering, learn to interpret the output\u2019s cluster centers, and use inertia plots to select the right number of clusters. Next, we\u2019ll cover Hierarchical Clustering, where we\u2019ll use dendrograms to identify clusters and cluster maps to interpret them. Finally, we\u2019ll use DBSCAN to detect clusters and noise points and evaluate the models using their silhouette score. We\u2019ll also use DBSCAN and Isolation Forests for anomaly detection, a common application of unsupervised learning models for identifying outliers and anomalous patterns.<\/p>\n

You\u2019ll learn to tune and interpret the results of each model and visualize the anomalies using pair plots. Next, we\u2019ll introduce the concept of dimensionality reduction, discuss its benefits for data science, and explore the stages in the data science workflow in which it can be applied. We\u2019ll then cover two popular techniques: Principal Component Analysis, which is great for both feature extraction and data visualization, and t-SNE, which is ideal for data visualization. Last but not least, we\u2019ll introduce recommendation engines, and you\u2019ll practice creating both content-based and collaborative filtering recommenders using techniques such as Cosine Similarity and Singular Value Decomposition.<\/p>\n

What you\u2019ll learn<\/h3>\n