Module 1: Foundations of AI, Python & Data Handling
• Understanding Python: Building Strong Foundations for AI & Data Science
• Introduction to Artificial Intelligence: Concepts, Origins, and Evolution
• The Rise of AI: Modern Applications and Its Impact on Society
• Ethics in AI: Responsible Use, Transparency, and Governance
• Addressing Ethical Dilemmas: Bias, Fairness, and Accountability
• Regulatory, Legal, and Social Considerations of AI Deployment
• Preparing Data for AI: Cleaning, Transformation, and Standardization
• Exploratory Data Analysis (EDA): Uncovering Patterns and Insights

Module 2: Core Principles of Machine Learning
• Understanding Machine Learning: Key Concepts and Definitions
• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• The Machine Learning Lifecycle: From Data Ingestion to Model Deployment
• Programming with Python: Essential Libraries for ML (NumPy, Pandas, Scikit-learn)
• Predictive Modeling: Introduction to Linear and Logistic Regression
• Evaluating Model Performance: Accuracy, Precision, Recall, and Beyond
• Practical Exercise: Build and Evaluate a Simple Regression Model

Module 3: Unsupervised Learning & Clustering Techniques
• Fundamentals of Unsupervised Learning: When Labels Aren’t Available
• Clustering Algorithms: K-Means, Hierarchical Clustering, and DBSCAN
• Reducing Complexity: Dimensionality Reduction with PCA
• Detecting Anomalies: Outlier Detection Methods in ML
• Hands-on Lab: Apply K-Means Clustering to Real-World Datasets

Module 4: Neural Networks and Deep Learning Essentials
• Neural Networks Demystified: Architecture, Neurons, and Activation Functions
• Learning Through Layers: Backpropagation and Optimization
• Deep Learning in Practice: Convolutional Neural Networks (CNNs)
• Vision Systems: Image Recognition and Classification
• Leveraging Pretrained Models: Introduction to Transfer Learning
• Hands-on Lab: Design and Train a CNN for Image Classification

Module 5: Natural Language Processing (NLP)
• Introduction to NLP: Bridging Human Language and Machines
• Text Preparation: Tokenization, Normalization, and Noise Removal
• Feature Representation: Bag-of-Words and TF-IDF Techniques
• Understanding Sentiment and Classifying Text Data
• Word Embedding Models: Word2Vec, GloVe, and Vector Representations
• Advanced NLP: Sequence-to-Sequence Models and Named Entity Recognition (NER)
• Hands-on Lab: Build a Sentiment Analysis Application