– Introduction to Artificial Intelligence and its evolution
– Types and functionalities of AI systems
– AI branches: Machine Learning, Deep Learning, Fuzzy Logic, Generative AI
– Applications of AI in NLP, Computer Vision, Robotics, and Business
– Real-world AI use cases and industry applications

– Linear Algebra fundamentals: vectors, matrices, eigenvalues
– Calculus concepts: derivatives, gradients, optimization
– Probability and Statistics for AI models
– Discrete Mathematics: logic, graph theory, combinatorics
– Mathematical foundations for Machine Learning algorithms

– Python programming fundamentals and syntax
– Control flow, functions, and data structures
– Working with NumPy and Pandas for data analysis
– Data visualization using Matplotlib and Seaborn
– Hands-on Python exercises for AI development

– Supervised and Unsupervised Learning techniques
– Regression, classification, and clustering algorithms
– Model evaluation and selection strategies
– Feature engineering and dimensionality reduction
– Hands-on Machine Learning model development

– Neural network architectures and frameworks
– Convolutional Neural Networks (CNNs)
– Recurrent Neural Networks (RNNs) and sequential processing
– Object detection models: YOLO and SSD
– Hands-on Deep Learning projects

– Fundamentals of image processing and transformations
– Image segmentation and Generative Adversarial Networks (GANs)
– AI applications in healthcare and autonomous systems
– Hands-on Computer Vision implementation projects

– Text preprocessing and tokenization techniques
– Word embeddings and language representation models
– Text classification and sentiment analysis
– Named Entity Recognition (NER) and Question Answering systems
– Hands-on NLP project implementation

– Fundamentals of Reinforcement Learning
– Agents, environments, rewards, and policies
– Q-Learning and Deep Q-Networks (DQN)
– Policy Gradient Methods and optimization
– Hands-on AI projects for gaming and robotics

– Introduction to AWS, Azure, and Google Cloud Platform
– Cloud Machine Learning services and deployment tools
– AutoML and pre-trained AI models
– Hands-on cloud-based AI project deployment

– Understanding LLM architectures and training concepts
– Text generation and language translation
– Knowledge extraction and conversational AI systems
– Practical applications of Generative AI and LLMs

– Neuro-Symbolic AI concepts
– Explainable AI (XAI) and AI interpretability
– Meta-Learning and Few-Shot Learning
– Federated Learning and decentralized AI systems
– Emerging trends in advanced AI research

– Presenting AI projects professionally
– Documenting AI systems and workflows
– Ethical AI principles and responsible AI practices
– Bias, fairness, transparency, and accountability in AI applications