Basic Course This syllabus provides a foundational understanding of AI and ML concepts, preparing students for further exploration and specialization in the field. Lesson 1: Introduction to AI – Definition and history of AI – Types of AI (Narrow, General, Superintelligence) – Applications and industries impacted by AI Lesson 2: Machine Learning Fundamentals – Introduction to Machine Learning (ML) – Types of ML (Supervised, Unsupervised, Reinforcement Learning) – ML workflow and key concepts Lesson 3: Data Preprocessing – Importance of data quality and preprocessing – Handling missing data, outliers, and feature scaling – Data visualization and exploration Lesson 4: Supervised Learning – Regression and classification problems – Linear Regression, Logistic Regression, Decision Trees – Model evaluation metrics (accuracy, precision, recall, F1 score) Lesson 5: Unsupervised Learning – Clustering (K-Means, Hierarchical Clustering) – Dimensionality reduction (PCA, t-SNE) – Anomaly detection and applications Lesson 6: Neural Networks – Introduction to Neural Networks (NNs) – Feedforward NNs, activation functions, and backpropagation – Basic NN architectures (MLP, CNN, RNN) Lesson 7: Deep Learning – Convolutional Neural Networks (CNNs) for image classification – Recurrent Neural Networks (RNNs) for sequence data – Long Short-Term Memory (LSTM) networks Lesson 8: Natural Language Processing – Text preprocessing and feature extraction – Sentiment analysis, text classification, and topic modeling – Introduction to language models and chatbots Lesson 9: Model Deployment and Ethics – Deploying ML models in production – Model interpretability and explainability – AI ethics, bias, and fairness considerations Lesson 10: Project and Case Studies – Hands-on project: applying AI concepts to a real-world problem – Case studies: AI applications in various industries (healthcare, finance, transportation)

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