Course Objectives:

• Introduce fundamental concepts of AI and machine learning.
• Equip students with Python programming skills necessary for AI development.
• Provide hands-on experience building simple AI models.

Course Outline:

Day 1: Introduction to AI and Machine Learning

• What is AI and machine learning?
• Types of AI: Narrow AI, General AI, Superintelligence
• Applications of AI in various fields
• The role of Python in AI development

Day 2: Python Programming Basics

• Introduction to Python
• Data types (numbers, strings, lists, tuples, dictionaries)
• Variables and operators
• Control flow (if-else statements, loops)
• Functions

Day 3: Data Structures and Libraries

• Numpy arrays
• Pandas DataFrames
• Matplotlib for data visualization
• Introduction to other relevant libraries (Scikit-learn, TensorFlow, Keras)

Day 4: Data Preprocessing and Feature Engineering

• Data cleaning and handling missing values
• Data normalization and standardization
• Feature selection and extraction
• Train-test split

Day 5: Supervised Learning: Regression

• Linear regression
• Multiple linear regression
• Polynomial regression
• Model evaluation metrics

Day 6: Supervised Learning: Classification

• Logistic regression
• Decision trees
• Random forests
• Support vector machines
• Model evaluation metrics

Day 7: Unsupervised Learning

• Clustering algorithms (K-means, hierarchical clustering)
• Dimensionality reduction (PCA)

Day 8: Neural Networks

• Introduction to neural networks
• Perceptrons
• Multilayer perceptrons
• Training neural networks (backpropagation)

Day 9: Deep Learning

• Convolutional neural networks (CNNs) for image recognition
• Recurrent neural networks (RNNs) for sequential data
• Transfer learning

Day 10: Project and Presentation

• Assignment: Build a simple AI application using Python and the learned techniques.
• Assignment presentations and feedback

Additional Considerations:

• Hands-on exercises: Incorporate practical coding exercises throughout the course to reinforce learning.
• Case studies: Discuss real-world examples of AI applications to provide context and inspiration.
• Guest lectures: Invite industry experts to share their experiences and insights.
• Online resources: Recommend online resources for further learning and practice.

By following this syllabus, students will gain a solid foundation in AI development using Python and be well-prepared to explore more advanced topics and projects.