Software to be learn:
•  Google Colab
•  Jupyter Nodebook

Week 1-2: Introduction to Artificial Intelligence & Data Preprocessing
• Introduction to AI and its history
• The AI revolution: Current applications and impact
• Overview of the course structure and expectations
• AI Ethics and Responsible AI
• AI Challenges: Bias, fairness, transparency
• AI in Society: Legal and ethical considerations
• Data Cleaning
• Exploratory Data Analysis

Week 3-4: Machine Learning Basics
• Introduction to Machine Learning
• Types of Machine Learning: Supervised, Unsupervised, Reinforcement
• The Machine Learning Workflow
• Python and libraries for Machine Learning
• Linear Regression
• Logistic Regression
• Model Evaluation and Metrics
• Hands-on: Building a Simple Linear Regression Model

Week 5-6: Unsupervised Learning and Clustering
• Unsupervised Learning Concepts
• Clustering Algorithms: K-Means, Hierarchical, DBSCAN Dimensionality Reduction: PCA
• Recommender Systems Anomaly Detection
• Hands-on: Implementing K-Means Clustering
Week 7-8: Neural Networks and Deep Learning
• Introduction to Neural Networks Perceptrons and Activation Functions Backpropagation
• Building a Feedforward Neural Network from Scratch
• Deep Learning Architectures: Convolutional Neural Networks (CNNs)
• Image Classification Transfer Learning
• Hands-on: Building a CNN for Image Classification
Week 9-10: Natural Language Processing (NLP)
• Introduction to NLP Text Preprocessing
• Bag-of-Words and TF-IDF Sentiment Analysis
• Word Embeddings: Word2Vec, GloVe Sequence-to-Sequence Models Named Entity Recognition
(NER)
• Hands-on: Building a Sentiment Analysis Model

Week 11-12: Advanced Topics in AI
• Reinforcement Learning Basics Markov Decision Processes (MDPs) Q-Learning
• Deep Reinforcement Learning
• AI in Real-World Applications: Healthcare, Finance, Autonomous Vehicles
• AI Trends and Future Directions
• Final Project Presentations and Showcase