Udemy – Python Data Science: Unsupervised Machine Learning 2024-4

Published on: 2024-09-12 14:25:52

Categories: 28

Descriptions

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’ll 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’ll 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’ll fit, tune, and interpret 3 popular clustering models using scikit-learn. We’ll start with K-Means Clustering, learn to interpret the output’s cluster centers, and use inertia plots to select the right number of clusters. Next, we’ll cover Hierarchical Clustering, where we’ll use dendrograms to identify clusters and cluster maps to interpret them. Finally, we’ll use DBSCAN to detect clusters and noise points and evaluate the models using their silhouette score. We’ll also use DBSCAN and Isolation Forests for anomaly detection, a common application of unsupervised learning models for identifying outliers and anomalous patterns.

You’ll learn to tune and interpret the results of each model and visualize the anomalies using pair plots. Next, we’ll 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’ll 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’ll introduce recommendation engines, and you’ll practice creating both content-based and collaborative filtering recommenders using techniques such as Cosine Similarity and Singular Value Decomposition.

What you’ll learn

Master the foundations of unsupervised Machine Learning in Python, including clustering, anomaly detection, dimensionality reduction, and recommenders
Prepare data for modeling by applying feature engineering, selection, and scaling
Fit, tune, and interpret three types of clustering algorithms: K-Means Clustering, Hierarchical Clustering, and DBSCAN
Use unsupervised learning techniques like Isolation Forests and DBSCAN for anomaly detection
Apply and interpret two types of dimensionality reduction models: Principal Component Analysis (PCA) and t-SNE
Build recommendation engines using content-based and collaborative filtering techniques, including Cosine Similarity and Singular Value Decomposition (SVD)

Who this course is for

Data scientists who want to learn how to build and interpret unsupervised learning models in Python
Analysts or BI experts looking to learn about unsupervised learning or transition into a data science role
Anyone interested in learning one of the most popular open source programming languages in the world