Enroll Course: https://www.udemy.com/course/ittensive-machine-learning-clustering/
In the ever-expanding universe of data science, understanding unsupervised learning techniques is crucial for unlocking hidden patterns and identifying outliers. The Udemy course, ‘Машинное обучение: кластеризация и аномалии на Python’ (Machine Learning: Clustering and Anomalies in Python), offered by ITtensive, provides a comprehensive deep dive into these powerful methods.
This course is the second installment in their ‘Unsupervised Machine Learning’ series, and it certainly lives up to expectations. It uses a practical approach, centering around a real-world hackathon challenge from Yandex.Realty to predict listing exposure times. This hands-on application makes the learning process incredibly engaging and relevant.
The course is meticulously structured into four distinct parts.
**Part 1: The Foundation**
This section lays a solid groundwork, guiding learners through every stage of data work. From defining tasks and their objectives to understanding fundamental machine learning model building principles, basic metrics, and introductory models like linear regression and ensembles, this part ensures you have a strong theoretical base before diving into more complex algorithms.
**Part 2: Core Clustering Models**
Here, the course delves into essential clustering techniques. You’ll explore both external and internal clustering metrics, master algorithms like K-Means and FOREL, and get hands-on experience with their application. The principles of agglomerative clustering are demystified, and you’ll learn about Mahalanobis distance and Gaussian Mixture Models (GMM). The practical assignment involves building a simple clustering model for raw data.
**Part 3: Advanced Clustering Techniques**
This is where the course truly shines, introducing advanced clustering methods. You’ll gain a deep understanding of the nuances between DBSCAN, HDBSCAN, and OPTICS, explore the proximity propagation model, and work with growing self-organizing neural gas. A significant portion is dedicated to Kohonen’s Self-Organizing Maps (SOM), Kirchhoff matrices, and spectral clustering. The culmination of this section is building an ensemble of multiple clustering models, showcasing a sophisticated approach to data segmentation.
**Part 4: Anomaly Detection Mastery**
The final segment focuses on the critical area of anomaly detection. You’ll learn about the pAUC metric, apply the Smirnov-Grubbs test, and practice ellipsoidal approximation. The course clarifies the distinctions between LOF and ABOD, and you’ll train and utilize the COPOD model. Furthermore, you’ll work with both Isolation Forest (iForest) and its extended version. The course concludes by assembling a complete solution for the 2020 Hackathon challenge, bringing all learned concepts together.
**Recommendation:**
For anyone looking to master clustering and anomaly detection in Python, this course is an excellent choice. The blend of theoretical knowledge with practical application on a real-world problem makes it highly effective. Whether you’re a beginner looking to explore unsupervised learning or an intermediate data scientist seeking to deepen your expertise, this course offers valuable insights and practical skills.
**Note:** To access the ITtensive courses on Udemy, you need to email support@ittensive.com with the name of the course or course bundle you wish to enroll in.
Enroll Course: https://www.udemy.com/course/ittensive-machine-learning-clustering/