Enroll Course: https://www.udemy.com/course/ittensive-machine-learning-clustering/
If you’re looking to deepen your understanding of unsupervised machine learning techniques, the ‘Машинное обучение: кластеризация и аномалии на Python’ course on Udemy is an excellent choice. This course is part of the ITtensive series and specifically targets learners interested in mastering data clustering and anomaly detection.
The course is thoughtfully divided into four detailed sections, starting with fundamental data processing and moving toward advanced clustering methods. In the initial part, you will learn essential data preprocessing, model evaluation, and foundational algorithms like linear regression and ensemble methods. The second part introduces basic clustering models such as K-means, FOREL, and hierarchical clustering, complemented by practical exercises that reinforce learning.
Progressing further, the third section explores sophisticated clustering techniques like DBSCAN, HDBSCAN, OPTICS, and neural network-based clustering methods such as SOM and spectral clustering. These modules include hands-on projects to build robust models capable of handling complex data structures.
The final part of the course focuses on anomaly detection, featuring advanced metrics like pAUC, Smirnov-Grubb’s test, and models like LOF, ABOD, COPOD, and iForest. The course culminates in a real-world hackathon project from 2020, offering valuable insights into applying these techniques to practical scenarios.
Overall, this course provides comprehensive coverage with a strong practical focus, making it suitable for data scientists, AI enthusiasts, and anyone aiming to enhance their machine learning toolkit. I highly recommend it to those willing to invest time in mastering clustering and anomaly detection on Python. Ensure you contact support@ittensive.com for access details, as this course is part of the exclusive ITtensive series on Udemy.
Enroll Course: https://www.udemy.com/course/ittensive-machine-learning-clustering/