Artificial Intelligence Fundamentals

A Clear and Practical Introduction to AI to Drive Better Business Decisions

New

Created byJan Willem Mooren

Last updated 5/2026

English

What you'll learn

Explain the fundamental concepts of artificial intelligence, including key terms such as machine learning, deep learning, and neural networks.
Describe how common AI models, including neural networks, Generative Adversarial Networks, and Transformers, function at a high level.
Identify practical use cases of AI across industries such as business, healthcare, and technology.
Apply basic AI concepts to evaluate how AI can be used to solve real-world problems.
Differentiate between various AI approaches and models, understanding their strengths and limitations.
Analyse the challenges and risks associated with AI, including ethical considerations, bias, and resource requirements.
Evaluate AI solutions and tools to determine their suitability for specific business or operational needs.

Course content

9 sections • 59 lectures • 3h 5m total length

Course Introduction2:19

Types of Artificial Intelligence0:11
Explore the types of artificial intelligence within the artificial intelligence fundamentals course, clarifying how different AI approaches relate to real-world applications.
Types of AI2:17
Explore the main AI types, from reactive and limited memory systems to theory of mind and the theoretical self-aware AI, with examples like games and self-driving cars.
Reactive AI2:57
Reactive AI machines respond instantly to the current situation with sensors, control systems, and actuators in real time, but cannot plan ahead or learn from past data.
Limited Memory AI2:21
Harness limited memory AI to maximize performance on devices with small data and compute. Utilize clustering, decision trees, and reinforcement learning for mobile, IoT, and autonomous systems with efficient processing.
Theory of Mind AI2:43
Autonomous Vehicle1:05
Autonomous vehicles sense their surroundings with cameras, radar, LiDAR, and ultrasonic sensors in a 360-degree view. AI and machine learning interpret data in real time to drive safely.
Self Aware AI4:13

Applications of AI0:17
Revolutionizing Industries2:15
Ai is transforming healthcare and finance in practice, improving diagnosis, personalized medicine, fraud detection, and risk assessment, while speeding drug discovery and automating routine tasks.
Natural Language Processing I2:13
Explore how natural language processing enables computers to understand and generate language, powering chatbots and virtual assistants. Experience sentiment analysis, speech recognition, and real-time translation shaping business insights.
Natural Language Processing II3:05
Explore how chatbots and virtual assistants use NLP to understand context and intent, generate coherent text, and power AI-powered search and named entity recognition.
Image Recognition2:47
Robotics2:45
Recommender Systems4:37
Gaming3:29
Transform gaming with intelligent NPCs and procedural content, creating dynamic environments and personalized gameplay. Enable AI-powered chatbots, realistic graphics, and game analytics to further enhance immersion and developer insights.
Finance6:10
Explore how artificial intelligence transforms finance by enhancing fraud detection, investment management, credit risk assessment, trading, and customer service, while improving personal budgeting, claims processing, and compliance.
Healthcare6:18
Artificial intelligence transforms healthcare by improving patient outcomes, enabling real-time monitoring, personalized treatment, and streamlined operations, including drug discovery and predictive clinical decision support.
Transportation7:08
Artificial intelligence powers autonomous vehicles, real-time traffic management, predictive maintenance, supply chain optimization, and personalized travel planning to improve safety, efficiency, and user experience.

Machine Learning0:11
Introduction to Machine Learning5:50
Learn how machine learning, a core AI pillar, learns from data to identify patterns and make predictions using supervised, unsupervised, reinforcement, and deep learning; explore real-world applications and key challenges.
Types of Machine Learning1:45
Explore supervised learning with labeled data to predict outputs, unsupervised learning with unlabeled data to identify patterns, and reinforcement learning via interaction and rewards to improve decisions.
Supervised Learning4:32
Unsupervised Learning4:14
Reinforcement Learning4:13
Regression Analysis4:42
Classification2:02
Classification Algorithms3:11
Challenges1:40
Evaluation Metrics2:56
Applications0:48
Explore how classification drives image and video labeling, organizes text in natural language processing, and supports medical diagnosis, fraud detection, and customer segmentation for tailored services.
Clustering3:55
Explore clustering as an unsupervised learning technique, covering hierarchical, k-means, and density-based algorithms, and apply it to data analysis, image processing, and recommendations while considering distance metrics.

Deep Learning0:11
Explore deep learning within artificial intelligence fundamentals, providing a clear overview for learners and highlighting foundational ideas essential to mastering the topic.
Introduction to Deep Learning1:26
Neural Networks4:03
Applications1:40
Explore how deep learning powers computer vision for object recognition and facial identification, natural language processing for translation and sentiment analysis, speech recognition, and robotics, including self-driving cars.
Challenges and Future Directions1:11
Explore the current challenges of deep learning, including data demands, hardware requirements, and overfitting, and discover future directions like explainable AI and transfer learning.
Convolutional Neural Networks7:28
Recurrent Neural Networks5:17
Explore recurrent neural networks that excel with sequential data, using feedback loops and backpropagation through time to handle variable-length sequences, address vanishing gradients, and employ LSTM or GRU variants.
Autoencoders6:39
Generative Adversarial Networks I2:17
Explore generative adversarial networks, where a generator creates data and a discriminator decides real or fake in a competitive loop that improves both models through discriminator-first training and batch normalization.
Generative Adversarial Networks II2:47
Transformer Networks2:48

Ethics in AI0:11
Explore ethics in ai within the artificial intelligence fundamentals course, highlighting how ethics in ai informs study and application of artificial intelligence fundamentals.
Introduction to AI Ethics2:52
Privacy and Security Concerns4:31
Explore how artificial intelligence expands capability while addressing privacy and security concerns, bias, deepfakes, surveillance, misinformation, and the ethical responsibilities of future AI professionals.
Bias in AI3:36
Regulations4:38
Explore how AI regulations address accountability, transparency, and bias, detailing national, regional, and international frameworks and the need for ongoing updates to balance ethics and innovation.

Future of AI0:11
Explore the future of ai and the fundamentals that shape its development, applications, and impact.
The Future of AI4:43
Future Trends and Impact6:14
Explore where artificial intelligence is heading and how data, hardware, and software advances drive smarter systems that transform healthcare, transportation, education, and entertainment.
Opportunities4:12
Explore how artificial intelligence boosts efficiency through automation, enables data-driven decisions, and personalizes experiences while balancing innovation with responsibility across healthcare, manufacturing, and customer interactions.
Challenges4:55

Requirements

There are no formal prerequisites for taking this course. It is designed for beginners who are new to artificial intelligence. A basic understanding of general computer use and familiarity with everyday technology will be helpful. No prior experience in programming, data science, or mathematics is required. Learners should have access to a computer and a stable internet connection to follow the lessons and explore the examples. Most importantly, bring curiosity and an interest in understanding how AI is shaping the world around us.

Description

This course contains the use of artificial intelligence, to enhance the learning experience of students.

Artificial Intelligence is no longer a future concept — it is already reshaping industries, transforming jobs, and influencing the way decisions are made every day. The real question is no longer whether AI matters, but whether you understand how it works and how to use it effectively.

This course, Artificial Intelligence Fundamentals, is designed to give you a clear, structured, and practical understanding of AI without overwhelming you with technical complexity. You will learn the core concepts behind artificial intelligence, including machine learning, deep learning, neural networks, Generative Adversarial Networks, and Transformer models.

The focus is not on coding or complex mathematics, but on understanding. You will gain insight into how modern AI systems operate behind the scenes and how they are applied in real-world scenarios.

Throughout the course, you will explore practical use cases across industries, helping you connect theory to real value. You will also understand the limitations, risks, and ethical considerations that come with AI, enabling you to approach it with both confidence and responsibility.

By the end of this course, you will have a solid foundation that allows you to confidently discuss AI, evaluate opportunities, and make informed decisions in a rapidly evolving technological landscape.

If you want to understand AI in a clear, practical, and meaningful way, this course is for you.

Who this course is for:

This course is designed for beginners who want to understand artificial intelligence without getting lost in technical complexity. It is ideal for business professionals, managers, and decision-makers who want to understand how AI can be applied in their organisation. It is also suitable for students and career switchers who are exploring opportunities in AI and want a solid foundation before moving into more advanced topics. Entrepreneurs and innovators who are looking to leverage AI in their products or services will find practical value in this course. Finally, this course is for anyone curious about how technologies like ChatGPT, machine learning, and generative AI actually work behind the scenes.

Artificial Intelligence Fundamentals

What you'll learn

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Course content

Introduction1 lecture • 2min

Module I4 lectures • 12min

Module 27 lectures • 16min

Module 311 lectures • 41min

Module 413 lectures • 40min

Module 511 lectures • 36min

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Module 75 lectures • 20min

Module 82 lectures • 2min

Requirements

Description

Who this course is for: