Artificial Intelligence Fundamentals

Learningidol

Final Exam

Included in course price

• Disclaimer 01:00

  • 1: Disclaimer 01:00

• Lecture 1: Advanced Machine Learning 34:00

  • 2: Lesson-1 Introduction to Advanced Machine Learning Techniques 05:00
  • 3: Lesson-2 Deep Learning Fundamentals 05:00
  • 4: Lesson-3 Neural Networks Architectures 04:00
  • 5: Lesson-4 Advanced Optimization Techniques 04:00
  • 6: Lesson-5 Convolutional Neural Networks (CNNs) 04:00
  • 7: Lesson-6 Recurrent Neural Networks (RNNs) 04:00
  • 8: Lesson-7 Generative Adversarial Networks (GANs) 04:00
  • 9: Lesson-8 Reinforcement Learning 04:00

• Lecture 2: Natural Language Processing (NLP) 25:00

  • 10: Lesson-1 Introduction to Natural Language Processing 04:00
  • 11: Lesson-2 Text Preprocessing Techniques 03:00
  • 12: Lesson-3 Word Embeddings and Word2Vec 03:00
  • 13: Lesson-4 Recurrent Neural Networks for NLP 03:00
  • 14: Lesson-5 Sequence-to-Sequence Models 03:00
  • 15: Lesson-6 Attention Mechanisms 03:00
  • 16: Lesson-7 Transformer Architecture 03:00
  • 17: Lesson-8 Advanced NLP Applications 03:00

• Lecture 3: Computer Vision 20:00

  • 18: Lesson-1 Introduction to Computer Vision 03:00
  • 19: Lesson-2 Image Preprocessing Techniques 03:00
  • 20: Lesson-3 Convolutional Neural Networks (CNNs) for Image Classification 03:00
  • 21: Lesson-4 Object Detection and Localization 03:00
  • 22: Lesson-5 Image Segmentation 03:00
  • 23: Lesson-6 Generative Models for Image Synthesis 03:00
  • 24: Lesson-7 Advanced Computer Vision Applications 02:00

• Lecture 4: Reinforcement Learning and Robotics 22:00

  • 25: Lesson-1 Introduction to Reinforcement Learning (RL) 03:00
  • 26: Lesson-2 Markov Decision Processes (MDPs) 03:00
  • 27: Lesson-3 Dynamic Programming for RL 02:00
  • 28: Lesson-4 Model-Free Methods: Q-Learning, SARSA 02:00
  • 29: Lesson-5 Deep Reinforcement Learning 03:00
  • 30: Lesson-6 Applications of RL in Robotics 03:00
  • 31: Lesson-7 Sim-to-Real Transfer Learning 03:00
  • 32: Lesson-8 Ethical Considerations in AI and Robotics 03:00

• Lecture 5: Advanced AI Applications and Emerging Trends 22:00

  • 33: Lesson-1 AI in Healthcare 03:00
  • 34: Lesson-2 AI in Finance 03:00
  • 35: Lesson-3 AI in Autonomous Vehicles 03:00
  • 36: Lesson-4 AI Ethics and Bias Mitigation 03:00
  • 37: Lesson-5 Explainable AI 03:00
  • 38: Lesson-6 AI Governance and Regulation 03:00
  • 39: Lesson-7 Future Trends in AI 04:00

• Assessment 09:00

  • 40: Final Exam 09:00

Artificial Intelligence represents a convergence of computational theory, mathematical modelling, data science, and cognitive simulation. This course provides a comprehensive academic exploration of its foundational principles, methodological structures, and real-world implications.

The programme begins with advanced machine learning concepts. You will explore deep learning fundamentals, neural network architectures, and optimisation strategies that improve model performance. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and reinforcement learning models are examined conceptually, focusing on how each architecture processes information and adapts through training cycles. Emphasis is placed on understanding model logic rather than code execution alone.

Natural Language Processing (NLP) forms the second major pillar. You will study how Artificial Intelligence systems process human language through text preprocessing, vector representations, and word embeddings such as Word2Vec. Sequence-to-sequence models, attention mechanisms, and transformer architectures are analysed to understand how machines interpret context and generate language. Applications in translation, sentiment analysis, summarisation, and conversational systems are examined through theoretical frameworks.

Computer vision modules explore how machines interpret visual information. You will examine image preprocessing, CNN-based classification, object detection, segmentation techniques, and generative models for image synthesis. These topics highlight how visual perception is modelled computationally and applied across industries.

Reinforcement learning and robotics introduce Markov Decision Processes (MDPs), dynamic programming, Q-learning, SARSA, and deep reinforcement learning. The course explores how agents learn optimal behaviours through reward-based systems and how these principles apply to robotics and autonomous systems. Ethical considerations surrounding autonomous decision-making are integrated throughout this module.

Advanced AI applications broaden the discussion into healthcare, finance, autonomous vehicles, and predictive systems. You will examine bias mitigation strategies, explainable AI models, governance structures, and regulatory debates shaping the future of Artificial Intelligence. Emerging trends, including multimodal systems and adaptive learning frameworks, are also considered.

Assessment includes a structured evaluation and final examination designed to measure theoretical comprehension and analytical reasoning. Upon successful completion, learners receive a digital certificate of course completion, reflecting their academic engagement with Artificial Intelligence principles.

This course provides theoretical knowledge and academic understanding only. It does not confer any professional status, licence, or right-to-practise, nor does it guarantee employment outcomes.

This course is suitable for:

• Learners interested in Artificial Intelligence theory and emerging technologies
• Students preparing for advanced study in computing or data science
• Professionals seeking structured understanding of AI systems
• Researchers exploring machine learning concepts
• Individuals interested in ethical and governance dimensions of AI

It is designed for those who wish to understand Artificial Intelligence beyond headlines, developing conceptual and analytical depth.

There are no formal entry requirements. However, learners will benefit from basic familiarity with mathematics, logical reasoning, and general computing concepts. A foundational understanding of Artificial Intelligence terminology is helpful but not mandatory.

Access to a reliable internet connection and a suitable digital device is required. Learners should commit to completing the structured assessment and final examination to gain full academic benefit from the programme.

Completion of this Artificial Intelligence course may support progression into junior AI support roles, data analysis assistance, research coordination, technical consultancy support, or further postgraduate study in computing and machine learning fields.

There are currently no Q&As for this course. Be the first to ask a question.