Foundations of Artificial Intelligence Online Course

Key Features

• Accredited by CPD
• Instant e-certificate
• FREE PDF + Hardcopy certificate
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• Fully online, interactive course with audio voiceover
• Self-paced learning and laptop, tablet, smartphone-friendly
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The Foundations of Artificial Intelligence course provides a complete and structured learning path for anyone eager to understand the principles, methods, and applications of AI. This course builds strong fundamentals in mathematics, machine learning, knowledge representation, natural language processing, computer vision, deep learning, and robotics. It is designed for learners who want to explore artificial intelligence from theoretical concepts to real-world applications. By covering essential AI techniques, algorithms, and models, this course empowers learners with practical skills to design intelligent systems. With each module, participants move step-by-step through the core areas of AI, ensuring they gain a well-rounded understanding of the field. The course prepares learners not only to comprehend artificial intelligence but also to implement and innovate with it

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Learning Outcomes

1. Understand core concepts and principles of artificial intelligence systems.
2. Apply mathematical foundations effectively in artificial intelligence models.
3. Analyze knowledge representation techniques for problem-solving in AI.
4. Build machine learning models and apply deep learning algorithms.
5. Implement natural language processing and computer vision applications.
6. Design AI-driven systems and robotics for real-world challenges.

Course Link : Foundations of Artificial Intelligence

Course Curriculum

Module 01 : Introduction to Artificial Intelligence

• Definition and history of AI
• Scope and applications of AI
• Types of AI: Narrow AI, General AI, Superintelligent AI
• Ethical considerations and AI challenges

Module 02 : Mathematics for AI

• Linear Algebra for AI models
• Probability and Statistics in AI algorithms
• Calculus for optimization techniques
• Graph theory and logic foundations

Module 03 : Knowledge Representation in AI – Part 1

• Propositional logic in AI
• Predicate logic and reasoning
• Semantic networks for AI knowledge representation
• Rule-based knowledge systems

Module 04 : Knowledge Representation in AI – Part 2

• Ontologies and frames in AI
• Probabilistic reasoning
• Temporal and spatial representation
• Knowledge graphs and applications

Module 05 : Machine Learning – Part 1

• Introduction to machine learning concepts
• Supervised learning techniques
• Regression and classification models
• Feature engineering and evaluation methods

Module 06 : Machine Learning – Part 2

• Unsupervised learning and clustering algorithms
• Reinforcement learning basics
• Ensemble learning methods
• Model selection and optimization

Module 07 : Deep Learning

• Introduction to neural networks
• Convolutional neural networks (CNNs)
• Recurrent neural networks (RNNs)
• Deep learning frameworks and applications

Module 08 : Natural Language Processing

• Text processing and tokenization
• Language models and embeddings
• Sentiment analysis and chatbot development
• Machine translation systems

Module 09 : Computer Vision

• Image recognition and object detection
• Image segmentation techniques
• Facial recognition systems
• Applications of computer vision in industry

Module 10 : Robotics

• Fundamentals of robotics and AI integration
• Path planning and navigation
• Robotic perception using sensors
• Applications in autonomous systems

Module 11 : Building AI Applications

• AI project lifecycle
• Designing intelligent systems
• Case studies of real-world AI applications
• Future trends in artificial intelligence

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Disclaimer:

This is an online course with pre-recorded lessons. You will get access to the course within 48 hours after your enrolment.