Machine Learning
We teach you to work with AI tools. These are exactly the specialists companies are looking for.
We help you write a resume and we practise interview technique with you.
An internship on real projects in a team.
Where our graduates work
Our students work at these well-known companies full time and on contract. A great many graduates also work at hundreds of other companies around the world.
Average salary by level
Your career path from a first job to expert. Salaries are current for the US market in 2025.
в PASV
Reviews from Machine Learning course graduates
Students start going through interviews while still taking the course. All our students who start job searching get employed within 1-2 months of searching.
About the profession
What you get on completion
- The course provides basic knowledge to enter the world of machine learning
- A promising field with high demand in the job market and attractive salaries
- We will teach Python programming and basic mathematics
- Interview training, resume assistance, and preparation for successful employment
- Internship in a commercial project to apply knowledge in practice
- Spouse learns for free in the same cohort
Who this course suits
Course syllabus
1. Learning Python
— Introduction to Python
— Python Fundamentals – Basics
1.2. Python Advanced
— Python Fundamentals – Advanced
— Programming Foundation
— External Utilities and Approaches
— Python Modules for Data Science
— Practical Project
— Interview Preparation
2. Introduction to Machine Learning. Definitions and Terminology
— Experience
— Label
— Splitting into Training, Test, and Validation Datasets
— Performance Metric
— Performance Metrics (SSE, MAE, Confusion Matrix, TRP, etc.)
— Underfitting and Overfitting
— Bias and Variance
— Cross-validation
— Imbalanced Data
3. Types of Machine Learning
— Transfer Learning
— Reinforcement Learning
— Existing Machine Learning Topics with Application Examples
4. Regression
— Confounding Variables
— Discrete and Continuous Variables
— Simple, Multiple, and Nonlinear Regression
— Variable Interactions. Model Quality: Residuals, R², p-value, Slope, and Intercept.
— Red Wine Quality Dataset
— OLS Linear Regression, ML Regression
— Regularization
— Hyperparameters
5. Classification
— Active Learning Algorithms
— Logistic Regression
— Support Vector Machines
— Decision Trees (BallTrees, KDTrees)
— Artificial Neural Networks
— Lazy Learning Algorithms
— K-Nearest Neighbors
— Random Forest
— Naive Bayes
— ROC Curve and AUC, Cumulative Accuracy Curve
— Credit Data
6. Ensemble Methods
7. Improvement Methods
— XGBoost
8. Model Tuning
— Bayesian Optimization
9. Clustering
— Proximity Measures
— Clustering Algorithms:
— Exclusive Clustering
— Overlapping Clustering
— Hierarchical Clustering
— Probabilistic Clustering
— Agglomerative Clustering
— Spectral Clustering
— K-Means
— Fuzzy K-Means
— Mini-Batch K-Means
— Gaussian Mixture
— BIRCH
— DBSCAN
— Mean Shift
10. ML Core - Introduction to Neural Networks (1)
11. ML Core - Introduction to Neural Networks (2)
12. ML Core - Introduction to Neural Networks (3)
13. ML Core - Introduction to Neural Networks (4)
14. Practical Application of Machine Learning (1)
15. Practical Application of Machine Learning (2)
16. Practical Application of Machine Learning (3)
17. Practical Application of Machine Learning (4)
18. Generative Models (1)
19. Generative Models (2)
20. APIs for Machine Learning
— Hugging Face
Answers to questions about the Machine Learning
Do I need programming experience before starting the course?
No. The course is designed for beginners. We start with the fundamentals and move gradually to more advanced topics.
How much time does the course take?
We recommend setting aside 15–20 hours a week: classes with an instructor, homework and independent practice.
How does the project internship work?
The internship takes place on real projects in the USA. You work in a team guided by experienced developers, gaining practical experience of commercial development.
Do you help with finding a job?
Yes. We provide full support: resume preparation, building a portfolio, mock interviews and guidance on the job search.
Which technologies will I learn on the course?
A full stack of modern technologies: JavaScript, React, Redux, Node.js, Express, MongoDB, TypeScript, Git and much more. The detailed syllabus is above on this page.