Artificial Intelligence Program

Description

This course provides a solid foundation in Artificial Intelligence, covering AI concepts, Python programming, data preprocessing, mathematics for AI, machine learning, and neural networks. Through theory and a hands-on mini project, students will learn to build and evaluate AI models and apply them to real-world problems while understanding ethical considerations.

Course Modules

Module 1: Introduction to Artificial Intelligence (6 Hours)
- Definition, scope, and objectives of AI
- History and evolution of AI
- Real-world applications across industries
- Differences between AI, Machine Learning, and Deep Learning
- Ethical considerations and responsible AI practices
- Overview of AI systems and components
Module 2: Python Programming for Artificial Intelligence (8 Hours)
- Python fundamentals: variables, data types, operators, and control structures
- Functions and modular programming
- Python modules and packages relevant to AI
- Introduction to AI libraries: NumPy, Pandas, Matplotlib
- Loading, inspecting, and manipulating datasets in Python
Module 3: Data Handling and Preprocessing (8 Hours)
- Structured vs unstructured data
- Data collection, cleaning, and standardization
- Handling missing or inconsistent data
- Normalization, transformation, and feature selection
- Preparing datasets for AI and machine learning models
Module 4: Mathematics and Statistics for AI (8 Hours)
- Linear algebra essentials: vectors, matrices, operations
- Probability concepts and distributions
- Descriptive statistics: mean, median, variance, standard deviation
- Data visualization techniques for understanding datasets
- Applying mathematical principles in AI algorithms
Module 5: Machine Learning Fundamentals (10 Hours)
- Introduction to machine learning concepts
- Supervised and unsupervised learning
- Regression, classification, and clustering techniques
- Training, validation, and testing datasets
- Model evaluation metrics: accuracy, precision, recall, F1-score
- Overview of dimensionality reduction
Module 6: Neural Networks and Mini AI Project (8 Hours)
- Fundamentals of neural networks and perceptron models
- Network architecture: neurons, layers, weights, and activation functions
- Training basics: forward propagation, loss function, gradient descent
- Mini AI project: dataset selection, preprocessing, model building, evaluation, and result interpretation

Learning Outcomes

By the end of this programme, learners will be able to:

Understand key concepts, applications, and ethical considerations of Artificial Intelligence.
Write Python programs and leverage AI libraries for data handling and model development.
Preprocess and transform structured and unstructured data for AI tasks.
Apply mathematical and statistical principles in AI and machine learning models.
Build, evaluate, and interpret basic machine learning models (regression, classification, clustering).
Understand neural network fundamentals and implement a mini AI project using real-world data.