Embark on a journey into unsupervised learning with our hands-on Python course "Python Data Science: Unsupervised Machine Learning.” From the concepts to laying down the workflow and right up to its implementation, we will take you through a comprehensive tour of data science. Examine various methods and use cases and discover how unsupervised learning relates to the rest of the data science field.
There are important pre-processing data steps that should be performed before developing the model, such as Moving to cluster with two major types of K-Means, Hierarchical Clustering, and another efficient method, DBSCAN. Understand metrics and visualization for model assessment and select the most appropriate one among the commonly used ones.
DBSCAN and isolation forests are used to detect anomalies and to apply complex dataset simplification via dimensionality reduction with PCA and t-SNE. Last but not least, go through recommendation engines and implement content-based and collaborative filtering using Python. Throughout the course "Python Data Science: Unsupervised Machine Learning,” take the position of an Associate Data Scientist employed to improve the software company’s approaches to retaining employees.
Python Data Science: Unsupervised Machine Learning Table of Contents:
- Introducing basic concepts of Unsupervised Machine Learning in Python, with topics such as clustering, anomaly detection, dimensionality reduction, and recommenders.
- Understand how to select and preprocess data for modeling in terms of feature engineering and standardization.
- Dive deep into three key clustering algorithms: The methods will include K-Means Clustering, Hierarchical
- Clustering and DBSCAN, with an optimal fitting of each, how to tune them, and how to analyze the results.
- Implement the ‘’Isolation Forest’’ and ‘’DBSCAN’’ families of algorithms that belong to unsupervised learning for anomaly detection.
- Employ and analyze the dimensionality reduction algorithms like PCA and t-SNE and attempt data simplification.
- Build recommendation engines using content-based and collaborative filtering methodologies, including processes like Cosine Similarity and Singular Value Decomposition (SVD).
Who is this course for?
- Data scientists who want to obtain proficiency in constructing and analyzing unsupervised learning models in Python.
- Interdisciplinary targeting data scientists and analysts who are interested in the unsupervised learning concept or are willing to change their occupation.
- Any person interested in mastering one of the globally used open-source programming languages.
