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SMART ANALYTICA KARAGANDA

9

Introduction to Artificial Intelligence

400 000 ₸
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Allocated 58 Quotas

The "Introduction to Artificial Intelligence" course is a six-month full-cycle program in artificial intelligence, designed for both beginners and students with prior programming experience, and built upon the best practices of the world's leading universities (Stanford, Princeton, HSE). The curriculum covers four modules: Python and OOP fundamentals, data cleaning and visualization (Pandas, Matplotlib, Seaborn, NumPy), classical machine learning (regression, classification, decision trees, random forest), and advanced topics - gradient boosting, clustering, neural networks, CNN, RNN, and transformers. The course's key advantage is its teaching staff: graduates of Warwick, Skoltech, and Purdue, award winners of international Kaggle competitions, and gold medalists for predicting Parkinson's disease. The fully online format ensures flexibility and accessibility from anywhere in the world, while personalized support via a Telegram group guarantees high-quality mastery of the material. The practical value of the course lies in working with real-world datasets and solving applied problems, as well as implementing algorithms from scratch, which fosters a deep understanding of how models work rather than merely the ability to call pre-built libraries.

Special condition

To successfully complete the course, students must complete at least two out of three homework assignments for each module, attend live lectures or watch their recordings, and pass the final test. In the event of withdrawal from the course or failure to meet the course requirements, a penalty of 100,000 KZT will be applied.

Course details

level

For beginner

Study format

Hybrid

Entrance exams

No

Duration, in weeks

26

Education language

Russian

Qualifications

junior

Skills


Upon completion of the course, students will confidently master Python and the key libraries for data analysis (Pandas, NumPy, Matplotlib, Seaborn), and will be able to efficiently preprocess, clean, and visualize data for machine learning tasks. Students gain proficiency in the full spectrum of algorithms, from linear and logistic regression to gradient boosting, clustering, and dimensionality reduction, as well as hands-on experience building neural networks, including CNNs, RNNs, and transformers using PyTorch. The graduate's portfolio will include defended projects based on real-world data along with skills in working with modern LLMs (GPT, LLaMA) and computer vision algorithms, enabling them to qualify for junior ML engineer positions in both the Kazakhstani and international job markets.

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