Artificial Intelligence and Machine Learning

Course Overview

This comprehensive course provides a rigorous foundation in AI and ML concepts, algorithms, and practical applications. Designed for both beginners and intermediate learners, it covers fundamental theories, programming implementations, and real-world case studies. Students will gain hands-on experience with Python, TensorFlow, and industry-standard tools while exploring ethical implications and cutting-edge advancements in the field.

Course Objectives

By completing this course, students will:

1. Understand core AI/ML concepts and their mathematical foundations

2. Master Python programming for data science and ML applications

3. Implement key algorithms including supervised/unsupervised learning and neural networks

4. Develop skills in data preprocessing, feature engineering, and model evaluation

5. Build competency in deep learning frameworks (TensorFlow/PyTorch)

6. Apply AI/ML techniques to real-world problems in computer vision and NLP

7. Evaluate ethical considerations and deployment challenges in AI systems

8. Create a professional portfolio of ML projects

Course Benefits

• Practical Skills: Hands-on labs with real datasets and industry tools
• Career Preparation: Direct pathway to roles like ML Engineer or Data Scientist
• Comprehensive Knowledge: From basic statistics to advanced neural networks
• Project Portfolio: Capstone project demonstrating end-to-end ML solutions
• Ethical Framework: Understanding of responsible AI development
• Certification: Recognized credential for career advancement

Course Outline

Module 1: Foundations of AI & ML

• History and evolution of AI

• Types of machine learning

• Mathematics for ML (linear algebra, probability)

Module 2: Python for Data Science

• Python programming essentials

• NumPy, Pandas, Matplotlib

• Data cleaning and visualization

Module 3: Supervised Learning

• Linear/logistic regression

• Decision trees and ensemble methods

• Model evaluation metrics

Module 4: Unsupervised Learning

• Clustering algorithms

• Dimensionality reduction

• Association rule learning

Module 5: Neural Networks

• Perceptrons and activation functions

• Backpropagation

• Hyperparameter tuning

Module 6: Deep Learning

• CNN for computer vision

• RNN for sequence data

• Transfer learning

Module 7: NLP Fundamentals

• Text preprocessing

• Word embeddings

• Transformer architectures

Module 8: Model Deployment

• Flask/Streamlit applications

• Cloud deployment (AWS/GCP)

• Model monitoring

Module 9: Ethics in AI

• Bias and fairness

• Explainable AI

• Regulatory considerations

Module 10: Capstone Project

• End-to-end ML solution

• Presentation and peer review

• Portfolio development

Assessment Structure

• Weekly coding assignments (40%)

• Midterm project (20%)

• Final capstone (30%)

• Participation (10%)

Learning Resources

• Required: Python 3.x, Jupyter Notebooks

• Recommended: Google Colab Pro, GitHub Student Pack

• Textbook: “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow”

This course balances theoretical foundations with practical implementation, preparing students for both academic advancement and industry roles in AI/ML.