Enroll Course: https://www.udemy.com/course/ittensive-machine-learning-clustering/
If you’re looking to deepen your understanding of machine learning, particularly in the realm of unsupervised learning, the course “Машинное обучение: кластеризация и аномалии на Python” is an excellent choice. Offered by ITtensive on Udemy, this course provides a comprehensive exploration of data clustering and anomaly detection using Python, and it is the second part of a series focused on unsupervised machine learning.
### Course Overview
The course is structured into four parts, each progressively building on the knowledge gained in the previous sections.
1. **Foundational Concepts**: The course begins with an introduction to the various types of tasks in machine learning and the formulation of these tasks. You’ll learn how to work with machine learning models to minimize predictive errors. Essential concepts such as basic metrics and simple models like linear regression and ensemble methods are also covered.
2. **Basic Clustering Models**: In this section, you will dive into fundamental clustering models. The course emphasizes both external and internal clustering metrics. You’ll work with K-means and FOREL models, practice their applications, and understand the principles behind agglomerative clustering. Additionally, you will explore the Mahalanobis distance and Gaussian Mixture Models (GMM).
3. **Advanced Clustering Techniques**: The third part takes a deeper dive into more advanced clustering methods. You will explore the differences between DBSCAN, HDBSCAN, and OPTICS models, and learn about proximity propagation. The course also introduces self-organizing maps (SOM), the Kirchhoff matrix, and spectral clustering, culminating in the creation of an ensemble of multiple clustering models.
4. **Anomaly Detection**: Finally, the course wraps up with a comprehensive look at anomaly detection. You will learn about the pAUC metric, implement the Smirnov-Grubbs test, and practice ellipsoidal approximation. The course also covers the differences between Local Outlier Factor (LOF) and Angle-Based Outlier Detection (ABOD), as well as training and applying the COPOD model and the isolation forest method.
### Why You Should Take This Course
This course is particularly beneficial for anyone interested in data science, as it not only covers theoretical aspects but also emphasizes practical applications through real-world examples, including a hackathon task from Yandex.Real Estate.
The instruction is clear and accessible, making complex concepts manageable for learners at various levels. Whether you are a beginner looking to start your journey in machine learning or an experienced data scientist wanting to refine your clustering and anomaly detection skills, this course has something to offer.
### Conclusion
Overall, “Машинное обучение: кластеризация и аномалии на Python” is a well-structured course that equips you with essential skills in unsupervised learning. By the end of the course, you will have a solid understanding of various clustering techniques and anomaly detection methods, as well as practical experience in applying these concepts to solve real-world problems.
Don’t miss out on this opportunity to enhance your machine learning expertise! For access to the course, please reach out to support@ittensive.com with the course name.
Happy learning!
Enroll Course: https://www.udemy.com/course/ittensive-machine-learning-clustering/