NO PREPAYMENT!

Artificial intelligence course

Neural networks, NLP, Computer Vision, Reinforcement Learning, GANs, Transformers. PyTorch, TensorFlow, Hugging Face. Jupyter notebooks with explanations.

from UAH 5,000 The term is from 10 days

Order in Telegram Order form

Areas of artificial intelligence

From classical machine learning to advanced generative models. Each project includes theoretical justification, implementation in Python, experiments and analysis of the results with visualization.

Neural networks (CNN, RNN)

Convolutional networks for images (ResNet, VGG, EfficientNet), recurrent for sequences (LSTM, GRU). Classification, object detection, time series forecasting.

from UAH 5,000

NLP / word processing

Sentiment analysis, text classification, Named Entity Recognition, machine translation, chat bots. BERT, GPT, T5, Hugging Face Transformers, spaCy.

from UAH 5,500

Computer Vision

Object recognition (YOLO, Faster R-CNN), segmentation (U-Net, Mask R-CNN), face recognition, OCR, classification of medical images. OpenCV, PyTorch Vision.

from UAH 5,500

GANs / generative models

Generative adversarial networks: DCGAN, StyleGAN, Pix2Pix, CycleGAN. Image generation, style transfer, super extension. Variational Autoencoders (VAE).

from UAH 7,000

Reinforcement Learning

Reinforcement learning: Q-Learning, DQN, PPO, A3C. Environments OpenAI Gym, Atari, robotics. Stable Baselines3, RLlib.

from UAH 6,000

Transformers / LLM

Transformer architecture, attention mechanism, fine-tuning BERT/GPT, RAG systems, LangChain for LLM applications, prompt engineering, embeddings.

from UAH 6,500

How we work on AI projects

Setting the problem

We analyze the TK, determine the type of task (classification, generation, etc.), choose the approach and architecture of the model

Data and preprocessing

We select or collect a dataset, perform EDA, cleaning, augmentation, feature engineering, normalization

Learning and assessment

We train the model, select hyperparameters, compare with the baseline, calculate quality metrics

Results and Protection

We visualize the results, prepare Jupyter notebooks with explanations, explain the code for protection

What you get

Jupyter notebooks with step-by-step explanations
Trained model with stored weights (.pth / .h5)
Learning graphs (loss, accuracy, metrics)
Confusion matrix, ROC-curve, F1-score, precision/recall

EDA (Exploratory Data Analysis) with visualizations
Comparison with baseline models and state-of-the-art
Google Colab notebook (works without GPU)
Defense preparation and free edits

Reviews of AI projects

"Course on Computer Vision - road sign recognition on PyTorch. CNN based on ResNet18, data augmentation, accuracy 97.5%. Jupyter notebook with detailed explanations. The commission was impressed!"

Bohdan P.

NTUU "KPI", Kyiv

"NLP-project: sentiment analysis of reviews in Ukrainian with BERT. Fine-tuning Hugging Face model, F1-score 0.89. Colab notebook works perfectly. Protected for the maximum score!"

Julia V.

LNU named after Franka, Lviv

"Ordered GAN for image generation - DCGAN on CelebA dataset. Training on Colab with GPU, results are impressive. There are loss graphs, examples of generated images. Everything is documented."

Roman Sh.

ONU named after Mechnikova, Odessa

All reviews

Frequently asked questions about AI courses

The most popular topics are: image classification (CNN on PyTorch or TensorFlow), sentiment analysis of text (NLP with BERT/Transformers), time series prediction (LSTM, Prophet), recommender systems (collaborative filtering), object recognition on video (YOLO), chatbots with LLM (LangChain + OpenAI API), image generation (GAN), reinforcement learning for game environments.

PyTorch is a standard in the scientific environment and education, has an intuitive Pythonic API, a dynamic calculation graph, easier debugging. TensorFlow/Keras is an industry standard, easier for beginners (Keras API), better support for mobile deployment (TensorFlow Lite). If the tutorial does not specify a specific framework, we recommend PyTorch for most coursework.

We select the appropriate dataset from open sources: Kaggle (thousands of datasets for any task), Hugging Face Datasets (NLP datasets), UCI Machine Learning Repository (classical datasets), Google Dataset Search, Papers With Code. We can also collect our own dataset through parsing or API.

In Jupyter notebooks, we add markdown cells with theoretical explanations: description of model architecture, mathematical justification (loss functions, optimizers, activations), explanation of preprocessing steps, interpretation of metrics. For an additional fee, we can prepare a full explanatory note according to the requirements of the university.

Classic ML (scikit-learn) — from 7 days. CNN for image classification — from 10 days. NLP with fine-tuning Transformers — from 10-14 days. GAN project — from 14 days. Reinforcement Learning — from 14 days. Terms include dataset selection, model training, hyperparameter selection, and documentation preparation with visualizations.