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CyberFormula3

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Artificial Intelligence and Machine Learning with Python

400 000 ₸
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Allocated 35 Quotas

The course prepares Junior-level specialists in machine learning and artificial intelligence using Python. The training is built around a single specialization: students progressively master Python, data preparation and analysis, mathematics and statistics for machine learning, classical ML algorithms, and neural networks. Each module concludes with a practical assignment using real-world datasets, while the entire course culminates in an end-to-end individual project — from problem definition and data preparation to model training, validation, and deployment as a working service. The theory is provided at the level necessary to understand how models work internally, rather than simply how to use ready-made libraries. Training is conducted online on the proprietary CyberFormula³ platform and includes live classes, pre-recorded materials, and weekly consultations with an instructor. Final assessment takes the form of a public project defense. Upon completion, students will be able to independently solve typical machine learning tasks and apply for Junior ML/AI Developer positions.

Special condition

To enroll in the course, a security deposit of 100,000 tenge is required. Upon successful completion of the training and passing the final assessment, the security deposit is fully refunded to the student. If the student terminates the training prematurely, the security deposit is non-refundable. In case of failure to pass the final assessment, the student must pay for re-testing at their own expense in the amount of 300,000 tenge. These terms are stipulated in a supplementary agreement with the student.

Course details

level

For all

Study format

Online

Entrance exams

Yes

Duration, in weeks

26

Education language

Russian

Qualifications

junior

Skills


Students will learn to program in Python and work with NumPy, Pandas, scikit-learn, and PyTorch libraries. They will be able to independently collect and clean data, including extracting training datasets using SQL queries, perform exploratory data analysis, and engineer features for machine learning models. Students will master the development, training, and validation of classical machine learning models and neural networks, including convolutional architectures, and will learn to correctly interpret performance metrics. By the end of the course, students will be able to take an ML task from problem definition through to a working service and present the final result to an assessment committee.

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