online course
Machine learning
clustering and anomaly
search methods
Cluster the data (k-means, DBSCAN) Find anomalies (isolated forest, PCA) Analyze data for monitoring and forecasting
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Clustering in Python
Clustering is a key technique in Data Science for grouping data based on similarities without predefined categories
Python with its powerful libraries has become the main tool for such tasks.
The demand for experts who know how to work with clustering in Python is growing.
They are needed to analyze data, search for patterns and anomalies in finance, marketing, logistics and other areas, which makes them one of the most in-demand on the market.
Who this course is for
Developers
Gain the basic skills to move into a new area of programming. Discover exciting challenges and increase your income.
Analysts
Master the experience of leading experts, quickly deepen your knowledge and reach a new professional level.
Beginning ML Professionals
Master data processing, machine learning models and neural networks. These skills will help you find interesting and promising jobs.
Clustering in Python
  • Understand the process and principles of building machine learning models.
  • Conduct exploratory data analysis (EDA) to identify patterns and anomalies.
  • Work with clustering methods, including Mahalanobis distance and Gaussian Mixtures (GMM).
  • Apply density clustering algorithms such as DBSCAN and HDBSCAN.
  • Use self-organizing Kohonen maps (SOM) for data visualization and analysis.
  • Master spectral clustering to deal with complex data structures.
  • Analyze and interpret results to improve the quality of models.
  • Apply the skills learned to real-world tasks such as data segmentation, anomaly detection, and forecasting.
Our experts are practicing professionals
with a solid track record in development
Maria Pegova
Data Science Lead at TechSolutions
8 years of experience in analytics and machine learning (Google, Microsoft, IBM). Expert in computer vision and NLP.
Nikolay Smirnov
CTO at AI Innovations
12 years in IT (Facebook, Tesla, Huawei). Specialist in reinforcement learning and autonomous systems.
Olga Ivanova
Head of Data Analytics at FinTech Global
9 years in financial analytics and predictive modeling (Goldman Sachs, Bloomberg, Mastercard). Expert in time series and risk management. Lead teacher of the Data-Driven Finance course on Udacity.
Evgeny Knysh
Senior ML Engineer at CloudTech
7 years of experience in developing ML solutions (Uber, Netflix, Spotify). Expert in recommendation systems and big data processing. Author of the popular course “Machine Learning in Production” on Coursera.
Ekaterina Morozova
Chief Data Scientist at HealthAI
10 years of experience in healthcare analytics and biomedical research (Pfizer, Mayo Clinic, WHO). Expert in healthcare data analytics and AI for diagnostics. Leads the AI in Healthcare program at FutureLearn.
Course Program
Introduction
4 Video lessons 1 Task
  • Welcome
  • Machine Learning Challenges
  • Learning without a teacher
  • Clustering tasks
  • Machine Learning Tasks
Part 1. Machine Learning Process
1.1. Processes
8 Video lessons 3 Tasks
  • Machine learning model and process
  • What is ETL
  • The process of machine learning
  • What is EDA
  • Data preparation
  • Sample splitting
  • Hyperparameter optimization
  • Undertraining and overtraining
  • Offset, scatter and data error
  • Model training
1.2. Linear models
6 Video lessons 4 Tasks
  • Maximum likelihood method
  • Least Squares Method
  • Approximation of gaps in the data
  • Data approximation
  • RMS error
  • Metrics and distances
  • Linear regression and L1/L2 regularization
  • Linear regression
Part 2. Basic clustering
2.1. Basic clustering
6 Video lessons 1 Task
  • External clustering metrics
  • Internal clustering metrics
  • K-means
  • Agglomerative clustering
  • FOREL
  • Mahalanobis distance
  • Test: Basic clustering
2.2. Workshop: Clustering of ads
8 Video lessons 1 Task
  • Prediction of ad exposure time
  • Data cleaning and preprocessing
  • Data enrichment
  • Factor extraction
  • K-means
  • Agglomerative clustering
  • GMM
  • Clustering metrics
  • Optimal number of clusters
Part 3. Clustering: advanced level
3.1. Advanced clustering
8 Video lessons 1 Task
  • DBSCAN
  • OPTICS
  • Affinity Propagation
  • Voronoi diagram
  • Expanding Neural Gas
  • Kohonen's self-organizing maps (SOM)
  • Kirchhoff matrix
  • Spectral clustering
  • Advanced clustering
3.2. Workshop: Clustering for classification
7 Video lessons 1 Task
  • DBSCAN
  • HDBSCAN
  • OPTICS
  • Affinity Propagation
  • Self-organizing Kohonen maps
  • Spectral clustering
  • Classification through clusters
  • Classification of ads
Part 4. Anomaly Search
4.1 The Anomaly Search Task
13 Video lessons 1 Task
  • Anomaly detection
  • Excess and asymmetry
  • Smirnov-Grubbs test
  • The pAUC metric
  • Ellipsoidal approximation
  • Local ejection factor (LOF)
  • ABOD
  • COPOD
  • Forest of Isolation (iForest)
  • Class balancing problem
  • SMOTE
  • ADASYN
  • Autocoders
Select the appropriate tariff plan
Initial
$10
Part 1: The Machine Learning Process Tutorials Practical assignments Chat with tutors Assignment Checkout Access - 1 month No certificate
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Basic
$18
2 part training program Video materials Practical assignments Chat with mentors Checking assignments Access to the course - 2 months No certificate
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Standard
$31
3 part training program Video materials Practical assignments Chat with tutors Checking assignments Access to the course - 6 months Certificate
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Complete
$55
4 parts of the training program Video materials Practical assignments Chat with mentors Checking assignments Access to the course - 12 months Certificate
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VIP
$97
4 parts of the training program Video materials Practical assignments Mentor support and coaching Assignment check Chat with students and tutors Access to the course - 12 months Certificate
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Certificate
After successfully finishing all lessons and hands-on tasks, you'll be awarded an official certificate to enhance your professional profile.
Return guarantee
We remain flexible to suit your needs. That's why we guarantee a full refund if you change your mind within three days
Testimonials from our alumni
Alexander
I liked the course. Especially useful were the modules on DBSCAN and spectral clustering - now I can easily work with large data sets and find hidden patterns. The practical assignments helped to consolidate the knowledge. Thanks to this course I was able to improve my skills and get a promotion at work, as now I actively apply these methods in analyzing data for our company.
Daniel
This course was a real breakthrough in my understanding of Machine Learning. I've been wanting to delve into clustering methods for a long time, and everything was explained here in a very accessible way. The course helped me not only to improve my skills but also to present my ideas confidently to management, which led to a promotion!
Angela
The course was amazing! I learned so much about anomaly detection and clustering methods! The material is presented in a structured way and the assignments help me to put theory into practice immediately. After completing the course I was able to optimize the processes in my company, which was noticed by my managers. This became one of the reasons for my promotion - now I am responsible for the implementation of ML-solutions in our department.
Konstantin
I have taken a lot of Machine Learning courses, but this one stands out for its practical orientation. Methods of clustering and anomaly search are explained on real cases, which makes the training as useful as possible. After the course I was able to propose new approaches to data analysis in our company.
Marat
The course turned out to be exactly what I needed for professional growth. I deepened my knowledge in clustering methods such as k-means and DBSCAN, and learned how to effectively search for anomalies in data.The practical assignments were challenging but very useful - they helped me to immediately apply the knowledge in real projects.Thanks to this course, I was able to improve my skills and get a promotion, as I now actively use ML methods to optimize business processes.
Answers to common questions
What documents will I receive at the end of the course?
You will receive a certificate of completion if you have completed all parts of the course and completed all assignments.
How many hours per week will I need to devote to the training on the platform?
It depends only on you. On average, platform users study between 8 and 12 hours per week.
What should I do if I find that the course isn't right for me?
If the course isn't what you expected, we offer a money-back guarantee. You can request a full refund after the first three lessons, and from the fourth lesson onward, we'll calculate the refund amount or assist you in selecting another course.
How does the training work?
You will be able to study the materials at your convenience, without taking time off work. The training package is based on modern, effective materials. Training and all materials are available anywhere, 24 hours a day, 7 days a week, within the time limits of your tariff.