Содержание курса
Machine Learning
10 уроков
1.
Supervised learning
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2.
Unsupervised learning
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3.
Decision tree
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4.
Neural network
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5.
Deep learning
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6.
Regression
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7.
Classification
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8.
Clustering
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9.
Feature engineering
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10.
Overfitting
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Neural Networks
10 уроков
1.
Neurons
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2.
Layers
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3.
Input layer
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4.
Hidden layers
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5.
Output layer
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6.
Activation function
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7.
Backpropagation
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8.
Training data
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9.
Testing data
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10.
Deep learning
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Natural Language Processing (NLP)
10 уроков
1.
Tokenization
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2.
Part-of-speech (POS) tagging
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3.
Named entity recognition (NER)
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4.
Stemming
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5.
Lemmatization
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6.
Stop words
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7.
Bag-of-words (BoW) model
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8.
Sentiment analysis
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9.
Topic modeling
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10.
Word embeddings
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Robotics
10 уроков
1.
Robot
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2.
Sensors
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3.
Actuators
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4.
Control system
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5.
Programming
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6.
Autonomy
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7.
Navigation
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8.
Manipulation
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9.
Human-robot interaction
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10.
Applications
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Computer Vision
10 уроков
1.
Computer Vision
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2.
Image processing
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3.
Image segmentation
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4.
Object detection
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5.
Object recognition
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6.
Feature extraction
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7.
Convolutional neural networks (CNNs)
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8.
Deep learning
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9.
Optical character recognition (OCR)
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10.
Augmented reality (AR)
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Expert Systems
10 уроков
1.
Expert Systems
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2.
Knowledge base
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3.
Inference engine
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4.
Rule-based systems
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5.
Fuzzy logic
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6.
Case-based reasoning
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7.
Natural language processing
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8.
Neural networks
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9.
Knowledge acquisition
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10.
Validation and verification
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Ethics in AI
10 уроков
1.
Ethics in AI
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2.
Bias
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3.
Transparency
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4.
Privacy
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5.
Accountability
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6.
Fairness
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7.
Human oversight
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8.
Cultural sensitivity
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9.
Regulation
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10.
Ethical dilemmas
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