Comprehensive Introduction to Artificial Intelligence

About Course

This course provides an in-depth introduction to Artificial Intelligence (AI), covering its history, core concepts, algorithms, applications, and ethical considerations. Students will gain hands-on experience with key AI techniques such as machine learning, neural networks, natural language processing, and computer vision. The course blends theory with practical assignments to prepare learners for advanced AI studies or entry-level AI roles.

Understand the fundamentals and categories of Artificial Intelligence
Learn how data is collected, cleaned, and used in AI systems
Master key algorithms that drive AI decision-making
Apply supervised, unsupervised, and reinforcement learning models
Build neural networks and understand deep learning frameworks
Work with Natural Language Processing (NLP) for language-based tasks
Implement image classification and object detection using computer vision
Analyze ethical issues and emerging trends in AI
Use Python and popular AI libraries for real-world projects
Prepare for entry-level roles or further study in AI and machine learning

Course Content

Artificial Intelligence: From Fundamentals to Applications
This comprehensive curriculum is designed to take learners on a structured journey through the field of Artificial Intelligence (AI), starting from its foundational principles to advanced applications. The course is divided into seven modules that progressively build technical knowledge and practical skills. Learners begin by understanding what AI is, its historical background, and real-world applications. The curriculum then explores essential building blocks such as data processing, algorithms, and the core techniques of machine learning (ML). As students advance, they delve into deep learning, natural language processing (NLP), and computer vision — each supported by hands-on coding projects and interactive lessons. The final module addresses ethical implications, societal impacts, and emerging trends in AI, empowering learners not only to build intelligent systems but also to do so responsibly. By the end of the course, students will have developed a solid conceptual understanding and practical experience with industry-standard tools like Python, TensorFlow, scikit-learn, and more — preparing them for further study or entry-level AI careers.

Lesson 1.1: What is Artificial Intelligence?

01:40:00
Lesson 1.2: Real-World Applications of AI

01:50:00

Module 2: Data and Algorithms — The Foundations of AI
This module lays the critical groundwork for understanding how AI systems operate by focusing on two key pillars: data and algorithms. Students will learn how data fuels AI systems and how algorithms act as the brain behind intelligent decision-making. You'll explore the entire lifecycle of data in AI—from collection and cleaning to transformation and feature extraction—ensuring a solid foundation in data preprocessing techniques. On the algorithmic side, learners will discover how machines solve problems and make decisions through logic, search techniques, and optimization. You'll be introduced to core algorithms including breadth-first search (BFS), depth-first search (DFS), and A*, along with fundamental concepts in heuristics and rule-based systems. By the end of this module, students will not only understand how raw data becomes intelligent insight but also gain practical experience building and applying basic algorithms using Python. This prepares you for more complex AI tasks in future modules like machine learning and deep learning.

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Comprehensive Introduction to Artificial Intelligence

About Course

What Will You Learn?

Course Content

Lesson 1.1: What is Artificial Intelligence?

Lesson 1.2: Real-World Applications of AI

Lesson 2.1: Understanding Data in AI

Lesson 2.2: Core Algorithms for Problem Solving

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