Learn AI with machine learning, deep learning, and real-world applications in Python and MATLAB
What you'll learn
✓ Explain AI concepts, types, characteristics, and key branches like ML, DL, NLP, Computer Vision, and Robotics.
✓ Describe ANN, CNN, RNN, Transformers, and LLMs, and understand how they process images, text, and sequential data.
✓ Relate AI concepts to real-world applications like fraud detection, recommendation systems, and image and speech processing.
✓ Explain AI system development steps, data types, basic implementation using Python/MATLAB, and intro to Generative AI.
Requirements
● No skills required . You will learn everything you want to understand about AI concepts
Description
"This course contains the use of artificial intelligence." Artificial Intelligence is transforming the way industries, businesses, and individuals solve problems, make decisions, and build smart systems. This course is designed to provide a broad and practical introduction to Artificial Intelligence (AI), covering both foundational concepts and modern advancements in the field. It is especially suitable for beginners, students, engineers, and anyone interested in understanding how AI works and how it is applied in real-world scenarios.
The course begins with the fundamentals of AI, including its meaning, characteristics, types, core components, and major branches such as Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, and Robotics. It explains how AI systems learn from data, recognize patterns, make predictions, and support intelligent decision-making across different domains. Learners will also understand different kinds of data used in AI, including structured, unstructured, semi-structured, and synthetic data.
A major focus of this course is on the important models and techniques used in AI. You will learn the concepts behind Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transformers, and Large Language Models (LLMs) in a simple and understandable manner. The course discusses how these models process images, text, speech, and sequential data, and why they are important in modern AI applications.
In addition to theory, this course emphasizes practical understanding through multiple real-world examples and implementations. It covers applications such as banking fraud detection, product recommendation systems, predictive maintenance, email spam detection, image recognition, road and medical image classification, and speech signal processing. These examples help learners connect AI concepts with practical industrial use cases. The course also includes programming-oriented demonstrations using Python and MATLAB, making it useful for both conceptual learning and hands-on exposure.
The course further introduces Generative AI, including how generative models create text, images, and code by learning patterns from data. It explains the role of Transformers, LLMs, and prompt engineering, giving learners a clear view of the technologies behind modern AI systems such as chatbots, virtual assistants, content generation tools, and intelligent coding assistants.
By the end of this course, learners will have a broad understanding of artificial intelligence, its major technologies, its applications across industries, and the basic workflow of building AI-based solutions. This course is ideal for those who want to build a strong foundation before moving into advanced AI, machine learning, deep learning, or generative AI projects.
Who this course is for
■ For beginners, students, engineers, and professionals seeking a foundational understanding of AI concepts, models, and real-world applications.


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