Fundamentals of Artificial Intelligence and Machine Learning
Description
Comprehensive introduction to artificial intelligence and machine learning concepts, terminology, types, and applications to establish foundational understanding for AI practitioners.
Learning Objectives
Learners will master fundamental AI and ML concepts including definitions, types of learning paradigms, neural networks, deep learning architectures, and the relationship between AI, ML, and deep learning. They will understand different data types, model training processes, inference methods, and evaluation metrics essential for AI applications.
Topics (10)
Deep dive into machine learning concepts, algorithms, and how ML enables systems to learn from data without explicit programming.
Comprehensive coverage of supervised learning including classification algorithms, regression techniques, training data requirements, and evaluation methods.
Exploration of unsupervised learning algorithms, clustering techniques, anomaly detection, and dimensionality reduction methods.
Introduction to reinforcement learning paradigm, agent-environment interaction, reward systems, and applications in autonomous systems.
Comprehensive study of artificial neural networks, deep learning architectures, backpropagation, and modern deep learning frameworks.
Foundational understanding of natural language processing, text analysis, language modeling, and NLP applications in AI systems.
Comprehensive coverage of data types, data quality, preprocessing methods, feature engineering, and data preparation for ML models.
Comprehensive overview of AI definition, historical development, major milestones, current state, and future prospects of artificial intelligence.
Introduction to computer vision techniques, image preprocessing, feature extraction, object detection, and image classification methods.
Detailed study of training methodologies, cross-validation, performance metrics, model selection, and evaluation best practices.