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This course covers 7 modules related to Artificial Intelligence (AI). Students will learn about Machine Learning, Neural Networks, Natural Language Processing (NLP), Robotics, Computer Vision, Expert Systems, and Ethics in AI. The course explores how computers can perform specific tasks without explicit instructions, understand human language, interpret visual information, solve complex problems, and make decisions.

Показатель Текущие показатели Рост
Значение 🏆 Рейтинг 3 дн 7 дн 30 дн
Количество учеников на курсе «AI Foundations: Building Blocks for a Smarter Future»Учеников на курсе 857
Сертификаты, выданные на курсе «AI Foundations: Building Blocks for a Smarter Future»Сертификатов выдано 0
Отзывы о курсе «AI Foundations: Building Blocks for a Smarter Future»Отзывов получено 13
Рейтинг курса «AI Foundations: Building Blocks for a Smarter Future»Рейтинг курса 4.462
Уроки в курсе «AI Foundations: Building Blocks for a Smarter Future»Количество уроков 70
Тесты в курсе «AI Foundations: Building Blocks for a Smarter Future»Количество квизов 10
Время прохождения курса «AI Foundations: Building Blocks for a Smarter Future»Время прохождения курса —
Обновления курса «AI Foundations: Building Blocks for a Smarter Future»Обновления курса — —
Дата публикации курса «AI Foundations: Building Blocks for a Smarter Future»Дата публикации курса — ———
Последнее обновление курса «AI Foundations: Building Blocks for a Smarter Future»Последнее обновление — ———
Сложность easy — ———

Содержание курса

Разделы в курсе «AI Foundations: Building Blocks for a Smarter Future» 7 разделов Уроки в курсе «AI Foundations: Building Blocks for a Smarter Future» 70 уроков Тесты в курсе «AI Foundations: Building Blocks for a Smarter Future» 10 тестов Время прохождения курса «AI Foundations: Building Blocks for a Smarter Future» 0 ч. Последнее обновление курса «AI Foundations: Building Blocks for a Smarter Future» обн. 13 января 2026

Machine Learning

10 уроков
1. Supervised learning ↗
2. Unsupervised learning ↗
3. Decision tree ↗
4. Neural network ↗
5. Deep learning ↗
6. Regression ↗
7. Classification ↗
8. Clustering ↗
9. Feature engineering ↗
10. Overfitting ↗

Neural Networks

10 уроков
1. Neurons ↗
2. Layers ↗
3. Input layer ↗
4. Hidden layers ↗
5. Output layer ↗
6. Activation function ↗
7. Backpropagation ↗
8. Training data ↗
9. Testing data ↗
10. Deep learning ↗

Natural Language Processing (NLP)

10 уроков
1. Tokenization ↗
2. Part-of-speech (POS) tagging ↗
3. Named entity recognition (NER) ↗
4. Stemming ↗
5. Lemmatization ↗
6. Stop words ↗
7. Bag-of-words (BoW) model ↗
8. Sentiment analysis ↗
9. Topic modeling ↗
10. Word embeddings ↗

Robotics

10 уроков
1. Robot ↗
2. Sensors ↗
3. Actuators ↗
4. Control system ↗
5. Programming ↗
6. Autonomy ↗
7. Navigation ↗
8. Manipulation ↗
9. Human-robot interaction ↗
10. Applications ↗

Computer Vision

10 уроков
1. Computer Vision ↗
2. Image processing ↗
3. Image segmentation ↗
4. Object detection ↗
5. Object recognition ↗
6. Feature extraction ↗
7. Convolutional neural networks (CNNs) ↗
8. Deep learning ↗
9. Optical character recognition (OCR) ↗
10. Augmented reality (AR) ↗

Expert Systems

10 уроков
1. Expert Systems ↗
2. Knowledge base ↗
3. Inference engine ↗
4. Rule-based systems ↗
5. Fuzzy logic ↗
6. Case-based reasoning ↗
7. Natural language processing ↗
8. Neural networks ↗
9. Knowledge acquisition ↗
10. Validation and verification ↗

Ethics in AI

10 уроков
1. Ethics in AI ↗
2. Bias ↗
3. Transparency ↗
4. Privacy ↗
5. Accountability ↗
6. Fairness ↗
7. Human oversight ↗
8. Cultural sensitivity ↗
9. Regulation ↗
10. Ethical dilemmas ↗