Introduction to Artificial Intelligence

• What is AI? Understanding its history, scope, and impact
• Key AI concepts: machine learning, deep learning, and NLP
• AI use cases across various industries

Machine Learning for AI

• Overview of supervised and unsupervised learning
• Working with classification, regression, and clustering algorithms
• Using Scikit-learn for building machine learning models

Neural Networks and Deep Learning

• Introduction to artificial neural networks (ANNs) and their applications
• Understanding how deep learning works and its applications in AI
• Building and training neural networks using TensorFlow and Keras

Natural Language Processing (NLP)

• Basics of NLP: tokenization, stemming, and lemmatization
• Building AI models for text classification and sentiment analysis
• Using NLP for chatbots, voice recognition, and language translation

Computer Vision and Image Processing

• Introduction to computer vision: object detection, image recognition
• Building models with convolutional neural networks (CNNs)
• Working with OpenCV for image and video processing

Reinforcement Learning

• Basics of reinforcement learning and decision-making algorithms
• Q-learning, policy gradient methods, and agent-based systems
• Applying reinforcement learning in robotics, gaming, and more

AI Frameworks and Tools

• Exploring AI development platforms like TensorFlow, Keras, and PyTorch
• Implementing AI models using cloud platforms such as AWS, Azure AI, and Google AI
• Best practices for developing and deploying AI applications

Ethics in AI

• Understanding the ethical implications of AI technologies
• Bias in AI: identifying and mitigating biased algorithms
• Privacy, security, and the societal impact of AI

Capstone Project: AI in Action

• Design, build, and deploy an AI model to solve a real-world problem
• End-to-end project development: data collection, model building, evaluation, and deployment
• Presenting insights and final AI model deployment