AI & ML
Course Title: Introduction to Artificial Intelligence and Machine Learning
Course Duration: 12 weeks
Module 1: Introduction to AI and ML
- Introduction to AI, ML, and Data Science
- The History and Evolution of AI
Module 2: Python and Data Manipulation
- Introduction to Python for Data Science
- Data Manipulation with Pandas
Module 3: Data Preprocessing and Visualization
- Data Cleaning and Preprocessing
- Data Visualization with Matplotlib and Seaborn
Module 4: Supervised Learning
- Linear Regression and Logistic Regression
- Decision Trees and Random Forests
Module 5: Unsupervised Learning
- Clustering (K-Means, Hierarchical, DBSCAN)
- Dimensionality Reduction (PCA, t-SNE)
Module 6: Deep Learning and Neural Networks
- Introduction to Neural Networks
- Deep Learning with TensorFlow/Keras
Module 7: Natural Language Processing (NLP)
- Text Processing, Tokenization, and Sentiment Analysis
- Introduction to NLP
Module 8: Computer Vision
- Introduction to Computer Vision
- Image Classification and Object Detection
Module 9: Reinforcement Learning
- Introduction to Reinforcement Learning
- Q-Learning and Deep Q Networks (DQNs)
Module 10: Special Topics
- Generative Adversarial Networks (GANs)
- Ethics and Bias in AI and ML
Module 11: Model Deployment and Real-world Applications
- Model Deployment with Flask or Docker
- Real-world AI and ML Applications
Module 12: Week 23-24: Capstone Project
- Project Ideation and Proposal
- Project Development and Presentation
Module 13: Final Exam and Course Wrap-up
Hands-on projects, assignments, and quizzes throughout the course to reinforce learning. Real-world datasets and case studies can also enhance the practical aspects of the course.