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Artificial Intelligence (2026)
Download this premium online course featuring high-quality video training, step-by-step lessons, practical demonstrations, and expert instruction. With Artificial Intelligence (2026), you'll gain practical knowledge through structured learning, hands-on examples, and real-world applications. This comprehensive eLearning resource is ideal for students, professionals, freelancers, and lifelong learners looking to develop valuable skills and stay current with modern industry practices at their own pace.
Published 7/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 4h 22m | Size: 1.71 GB
Basic Introduction to Data Structure and Search Algorithms
What you'll learn
infer knowledge in problem formulation with AI
exemplify the uninformed and informed search technique procedures for real world problems
understand the adversarial search methods, constraint satisfaction problems and intelligent agents
demonstrate various knowledge representation techniques
Requirements
Nil
Description
This course provides a comprehensive introduction to the fundamental concepts, techniques, and applications of Artificial Intelligence (AI). It is designed to equip learners with the knowledge required to formulate problems, represent knowledge, and develop intelligent solutions for real-world challenges. The course begins with an overview of AI, its techniques, models, learning aspects, and problem-solving methodologies. Students will explore state-space representation and solve classical AI problems such as Tic-Tac-Toe, Missionaries and Cannibals, and the Travelling Salesman Problem. The course introduces essential data structures, including stacks, queues, trees, and graphs, which serve as foundations for implementing search algorithms. Learners will study uninformed and informed search techniques such as Breadth-First Search, Depth-First Search, Uniform Cost Search, Best-First Search, and A* algorithms. Further, the course examines adversarial search methods, game-playing strategies, constraint satisfaction problems, and intelligent agents, emphasizing rational decision-making and agent environments. Knowledge representation techniques, including propositional logic, predicate logic, semantic networks, frames, and reasoning mechanisms, are covered to enable effective modeling of intelligent systems. Students will also gain insights into planning methods, machine learning fundamentals, and the architecture of expert systems. Through theoretical concepts, practical problem-solving approaches, and case studies, learners will develop the ability to analyze, design, and implement AI-based solutions. Upon successful completion, students will be capable of applying AI techniques to solve complex problems, representing knowledge effectively, designing intelligent agents, and developing expert systems that support decision-making across diverse domains. The course also fosters analytical thinking, computational reasoning, and lifelong learning skills essential for emerging AI technologies and future innovations.
Who this course is for
Beginners
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