Artificial Intelligence Courses

Day 1: ● Foundations of AI & Machine Learning: ○ Introduction to core AI concepts and terminology (machine learning, deep learning, natural language processing, computer vision) ○ Understanding different types of machine learning algorithms (supervised, unsupervised, reinforcement) ○ Exploring the ethical considerations of AI development and deployment ○ Hands-on: Implementing basic machine learning models with libraries like scikit-learn ● Python Programming for AI: ○ Introduction to Python and its essential libraries for AI (NumPy, Pandas, Matplotlib) ○ Understanding data structures and algorithms relevant to AI ○ Writing clean and efficient code for data manipulation and model development ○ Hands-on: Building simple AI applications using Python

Day 2: ● Natural Language Processing (NLP): ○ Introduction to NLP concepts like text analysis, sentiment analysis, topic modeling ○ Exploring techniques for working with text data (tokenization, stemming, lemmatization) ○ Building chatbots and sentiment analysis applications ○ Hands-on: Implementing basic NLP tasks using libraries like NLTK or spaCy ● Computer Vision (CV): ○ Introduction to CV concepts like image classification, object detection, image segmentation ○ Understanding convolutional neural networks (CNNs) and their role in CV ○ Building applications for image recognition and object detection ○ Hands-on: Implementing basic CV tasks using libraries like OpenCV or TensorFlow

Day 3: ● Deep Learning Fundamentals: ○ Introduction to neural networks and their architecture (perceptrons, hidden layers, activation functions) ○ Understanding the training process of neural networks (loss functions, optimizers) ○ Building and training simple deep learning models for classification and regression ○ Hands-on: Implementing basic deep learning models using libraries like Keras or PyTorch ● Generative AI & Reinforcement Learning: ○ Introduction to generative models like GANs and their applications ○ Understanding reinforcement learning concepts and its use in AI agents ○ Exploring applications of generative AI and reinforcement learning in various domains ○ Hands-on: Implementing simple generative models or reinforcement learning agents

Day 4: ● AI for Specific Applications: ○ Choose 2-3 trending application areas based on audience interest (e.g., healthcare AI, finance AI, robotics AI) ○ Deep dive into relevant AI methodologies and tools used in these domains ○ Showcase real-world case studies and successful AI implementations ○ Hands-on: Building mini-projects related to the chosen application areas ● AI Ethics & Responsible Development: ○ Discuss potential biases in AI algorithms and responsible development practices ○ Explore explainability and interpretability of AI models to ensure transparency ○ Address ethical considerations around data privacy and security ○ Group discussion and brainstorming solutions for responsible AI development

Day 5: ● Career Exploration & Portfolio Building: ○ Explore various career paths and opportunities in the AI field ○ Learn how to build a strong AI portfolio showcasing your skills and projects ○ Prepare for potential AI job interviews and technical assessments ○ Guest speaker or panel discussion from industry professionals (optional) ○ Mock interview session and personalized feedback