Module 1: Foundations of Artificial Intelligence

• Defining AI: Understanding what artificial intelligence really is, breaking myths and clarifying misconceptions. Exploring how AI differs from traditional software systems.
• History of AI: A timeline-based walkthrough of AI development since the 1950s, including the AI winters and the rise of deep learning.
• Branches of AI: Introduction to core branches—Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Expert Systems, and Robotics.
• Ethical Understanding of AI: Early introduction to the moral and social implications of AI technologies, setting a base for later deeper discussions.
• AI Use-Cases Across Industries: Case-based approach to learning how AI is applied in real industries including healthcare, finance, agriculture, automotive, security, retail, and manufacturing.

Module 2: Programming for AI Using Python

• Why Python for AI: Overview of why Python is the most popular language in AI development.
• Python Basics Refresher: Variables, loops, conditionals, data structures (lists, tuples, dictionaries), and functions.
• Object-Oriented Programming: Classes, objects, inheritance, encapsulation, and polymorphism.
• Working with Libraries: Introduction to essential AI libraries like NumPy (for numerical computing), Pandas (for data manipulation), Matplotlib & Seaborn (for data visualization).
• Jupyter Notebooks and IDEs: Hands-on with environments used by AI engineers.

Module 3: Data Handling and Preprocessing

• Types of Data in AI: Structured, semi-structured, and unstructured data.
• Data Cleaning Techniques: Removing null values, handling duplicates, correcting errors.
• Feature Engineering: Creating new features, encoding categorical variables, normalizing/scaling features.
• Data Splitting: Training, validation, and testing sets. K-Fold cross-validation.
• Data Pipelines: Automating preprocessing tasks using pipelines.

Module 4: Machine Learning Concepts

• Introduction to ML: What is machine learning, and its categories—supervised, unsupervised, and semi-supervised learning.
• Regression Algorithms: Linear Regression, Polynomial Regression, Ridge and Lasso Regression.
• Classification Algorithms: Logistic Regression, Decision Trees, Random Forest, Naive Bayes, Support Vector Machines (SVM), K-Nearest Neighbors (KNN).
• Clustering Algorithms: K-Means, Hierarchical Clustering, DBSCAN.
• Model Evaluation: Accuracy, precision, recall, F1 score, confusion matrix, ROC-AUC.
• Bias-Variance Tradeoff: Underfitting vs. overfitting.
• Model Tuning: Hyperparameter tuning using Grid Search and Random Search.

Module 5: Deep Learning & Neural Networks

• Understanding Deep Learning: What makes deep learning different from traditional ML.
• Neural Network Architecture: Perceptron, Multilayer Perceptrons (MLPs), forward and backward propagation.
• Activation Functions: Sigmoid, ReLU, Tanh, Softmax.
• Loss Functions: MSE, Cross-Entropy, Hinge loss.
• Optimizers: Gradient Descent, Stochastic Gradient Descent, Adam.
• Introduction to Frameworks: TensorFlow, Keras, PyTorch hands-on training.
• Convolutional Neural Networks (CNNs): Understanding how CNNs work for image data.
• Recurrent Neural Networks (RNNs): Introduction to sequence models.

Module 6: Natural Language Processing (NLP)

• Introduction to NLP: Understanding how computers process and understand human languages.
• Text Preprocessing: Tokenization, stopword removal, stemming, lemmatization.
• Vectorization Techniques: Bag of Words, TF-IDF, Word Embeddings (Word2Vec, GloVe).
• Language Modeling: N-grams, Markov Chains.
• Text Classification: Spam detection, sentiment analysis.
• Named Entity Recognition (NER) and POS tagging.
• Transformers and BERT: Introduction to attention mechanisms and transformer-based models.
• Building Chatbots: Rule-based and AI-based chatbot development.

Module 7: Computer Vision

• Digital Image Basics: Pixels, image formats, color models.
• Image Preprocessing: Resizing, normalization, filtering.
• Object Detection & Recognition: YOLO, SSD, Haar cascades.
• Face Recognition Techniques: Eigenfaces, LBPH, deep learning-based approaches.
• Using OpenCV: Basics and advanced techniques in image and video processing.
• Advanced CNN Architectures: ResNet, Inception, MobileNet.
• Projects: Implementing real-world computer vision projects (face mask detection, object counting, etc.).

Module 8: Reinforcement Learning

• Core Concepts: Agent, Environment, Action, State, Reward.
• Markov Decision Processes (MDPs): States, transitions, policies.
• Dynamic Programming Techniques: Policy iteration, value iteration.
• Q-Learning and SARSA: Tabular methods and policy updates.
• Deep Reinforcement Learning: DQN (Deep Q-Networks), Actor-Critic methods.
• Applications: AI for games, robotics, simulations.

Module 9: AI Ethics, Governance, and Society

• Understanding AI Ethics: Bias in AI, algorithmic fairness.
• Data Privacy & Security: How AI systems handle and protect data.
• AI in Governance: Use of AI by governments and regulatory frameworks.
• Accountability in AI: Explainable AI and transparency.
• Job Displacement & Human-AI Collaboration: Socio-economic impacts of AI adoption.
• Global Case Studies: Where AI failed ethically and lessons learned.

Module 10: Model Deployment and MLOps

• Deployment Basics: What it means to deploy an AI model.
• Model Saving & Versioning: Pickle, joblib, ONNX, TF Serving.
• Web Frameworks for Deployment: Flask, FastAPI, Streamlit.
• Introduction to MLOps: CI/CD pipelines, monitoring, scaling.
• Using Cloud Services: AWS Sagemaker, Google AI Platform, Microsoft Azure AI.
• Docker and Containers for AI Models: Creating portable AI applications.
• MLflow & DVC: Tools for tracking experiments and managing data.

Module 11: Capstone Project & Industry Readiness

• Project Planning: Identifying a real-world problem, preparing a project proposal.
• Solution Building: Data collection, preprocessing, model training and evaluation.
• Deployment & Presentation: Deploying the model with proper documentation.
• GitHub Portfolio Building: Documenting projects for recruiters.
• Mock Interviews and Resume Building: Preparing for AI-specific job roles.
• Freelancing and Consulting Skills: How to pitch AI solutions to clients.