Course Description

This Artificial Intelligence & Machine Learning course is designed to provide students with a strong foundation in AI concepts, Python programming, data science, and modern machine learning technologies. The course focuses on hands-on learning, real-world projects, and industry tools used by professional AI engineers.

Students will start by understanding the fundamentals of Artificial Intelligence, Machine Learning, and Deep Learning, followed by learning Python programming for data science. The program covers essential mathematical concepts required for machine learning, including linear algebra, probability, and statistics.

Learners will then explore data preprocessing techniques, including data cleaning, feature engineering, and dataset preparation. The course introduces core machine learning concepts such as supervised learning, unsupervised learning, and reinforcement learning, along with model evaluation techniques like accuracy, precision, recall, F1 score, and confusion matrix.

Students will gain practical experience with popular machine learning algorithms including:

• Linear Regression

• Logistic Regression

• K-Nearest Neighbors

• Decision Trees

• Random Forest

• Support Vector Machines

The curriculum also includes advanced topics such as Deep Learning, Computer Vision, and Natural Language Processing (NLP). Students will learn to build neural networks using TensorFlow, Keras, and PyTorch, and develop AI applications like image classifiers, sentiment analysis systems, and chatbots.

A dedicated module introduces Generative AI and Large Language Models (LLMs), covering transformer architecture, prompt engineering, and AI development using tools such as OpenAI, LangChain, and HuggingFace.

The course also focuses on MLOps and deployment, enabling students to deploy machine learning models using Flask or FastAPI, containerize applications with Docker, and deploy them on cloud platforms such as AWS, Google Cloud, and Microsoft Azure.