TRAINING COURSE: Introduction to Artificial intelligence

Understanding Data and Data Preprocessing

•       The Role of Data in AI
•       Types of data: Structured vs. unstructured data
•       Importance of data quality
•       Data Preprocessing
•       Data cleaning techniques
•       Feature engineering and normalization
•       Handling missing data
•       Hands-On Exercise
•       Data collection and preprocessing in Python using real datasets

Supervised Learning

•       Introduction to Supervised Learning
•       Regression vs. classification tasks
•       Key algorithms: Linear regression, logistic regression, k-nearest neighbors (KNN), decision trees
•       Model Evaluation
•       Metrics for evaluating supervised models (accuracy, precision, recall, F1-score)

Unsupervised Learning

•       Introduction to Unsupervised Learning
•       Key concepts: Clustering and association
•       Popular algorithms: K-means clustering, hierarchical clustering, DBSCAN
•       Dimensionality reduction techniques: PCA (Principal Component Analysis)

Neural Networks and Deep Learning

•       Introduction to Neural Networks
•       Basics of artificial neurons and neural networks
•       Understanding layers, activation functions, and weights
•       Deep learning and the role of deep neural networks
•       Types of Neural Networks
•       Feedforward neural networks
•       Convolutional Neural Networks (CNN)
•       Recurrent Neural Networks (RNN)
•       Building a basic neural network using TensorFlow/Keras

Natural Language Processing (NLP)

•       Introduction to NLP
•       What is Natural Language Processing?
•       Key challenges in NLP: Understanding and processing human language
•       Applications of NLP: Chatbots, sentiment analysis, text classification
•       NLP Techniques and Tools
•       Tokenization, stemming, lemmatization
•       Word embeddings (e.g., Word2Vec, GloVe)

AI in Computer Vision

•       Introduction to Computer Vision
•       What is computer vision and how does it relate to AI?
•       Key challenges in computer vision: Image recognition, object detection, segmentation
•       Applications of computer vision: Face recognition, autonomous vehicles, medical imaging

Reinforcement Learning

•       Introduction to Reinforcement Learning
•       What is reinforcement learning?
•       Key components: Agent, environment, reward, policy
•      Types of reinforcement learning: Model-free vs. model-based
•       Applications of reinforcement learning in gaming and robotics
•       Building a simple reinforcement learning agent using OpenAI Gym

Ethical Considerations and Future of AI

•       Ethics in AI
•       AI bias and fairness
•       Privacy concerns and data protection
•       The impact of AI on jobs and society
•       The importance of responsible AI development
•       The Future of AI
•       AI in the next decade: Trends and predictions
•       The role of AI in shaping future technologies