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Machine learning

595 000 ₸
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Allocated 32 Quotas

The course is designed to help students develop an understanding of the fundamental principles of supervised and unsupervised learning, as well as to build practical skills in applying machine learning algorithms to real-world problems. Students will learn to use methods of classification, clustering, and regression to solve tasks in fields such as robotics, text analysis, computer vision, medicine, and audio analytics. Special attention is given to ensemble methods — stacking, bagging, and boosting. The course covers best practices for model building and optimization, synthetic data generation, and the organization of a complete ML pipeline. Students will also become familiar with model monitoring tools and participate in simulated competitions modeled after Kaggle. The course emphasizes the practical application of acquired knowledge in real-life projects.

Special condition

No

Course details

level

For all

Study format

Online

Start

September

Entrance exams

No

Duration, in weeks

26

Duration in academic hours

390

Education language

Kazakh

Classes days_of_week

Mon-Fri

Teaching methodology

There are more practices than theories

Qualifications

Junior (Strong) machine learning specialist

Classes format

Online lessons once a week for 2 hours

Skills


Key Skills: Upon completion of the course, the student will be able to confidently formulate and interpret machine learning tasks, distinguish between supervised and unsupervised learning approaches. The student will master the skills of extracting and preprocessing data from various sources, and will learn to apply and compare popular classification, regression, and clustering algorithms. The student will master data visualization methods, feature selection and transformation, including dimensionality reduction using PCA. The student will also gain practical experience in building and configuring neural networks to solve applied problems in various subject areas.

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