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Artificial intelligence

500 000 ₸
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Allocated 17 Quotas

The "Artificial Intelligence" course is a practical program for anyone who wants to understand AI technologies and learn how to apply them to solve real-world problems. During the training, students progressively master Python, data analysis using Pandas and NumPy, classical machine learning algorithms, deep neural networks, and generative AI. Dedicated modules cover computer vision, transformer architectures, and modern generative models. The program is suitable both for beginners with no programming experience and for those who want to systematically build their knowledge in AI and machine learning. Training is conducted online in real time and combines theory with practical assignments based on real data. The course concludes with a final project covering the full cycle of AI solution development — from data collection and analysis to model building and presentation. Upon completion, graduates receive the Junior AI Engineer qualification and are prepared to work with modern AI-tools.

Special condition

The student receives an educational grant in the amount of 400 000 KZT, provided that they pay the remaining course fee difference of 100 000 KZT. The student is required to complete the full course, successfully finish the final project, and pass the final assessment with a score of no less than 50%. In case of failure to meet these requirements, the student must reimburse the full cost of the course. If the student misses more than 30% of classes within a calendar month without a valid reason, they may be expelled and will also be required to repay the full tuition cost.

Course details

level

For all

Study format

Online

Entrance exams

No

Duration, in weeks

27

Education language

Russian

Qualifications

Junior AI Engineer

Skills


• Proficiency in Python for AI tasks • Working with data using NumPy and Pandas • Conducting exploratory data analysis (EDA) and data visualization • Applying basic machine learning methods (classification and regression) using scikit-learn • Training and evaluating models using key performance metrics • Using classical machine learning algorithms (SVM, Decision Trees, Random Forest, k-NN, Naive Bayes) • Applying dimensionality reduction techniques (PCA) • Developing neural networks using PyTorch (including CNNs) • Basic application of transformers for natural language processing (NLP) tasks • Implementing the full AI project lifecycle: from data preparation to model evaluation

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