Professional Courses
Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.
Categories
Interactive Games
Fun, engaging games to boost memory, math fluency, typing speed, and English skills—perfect for learners of all ages.
Typing
Memory
Math
English Adventures
Knowledge
AI Course Fundamental
Explore and master the fundamentals of AI Course Fundamental
You've not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.Chapter 1
Introduction to Artificial Intelligence
Artificial Intelligence has evolved from ancient myths to a significant facet of modern technology, characterized by pivotal milestones such as the Turing Test and the Dartmouth Conference. Its classification into Narrow and General AI highlights the varying capabilities of intelligent systems. With applications spanning numerous industries including healthcare, finance, and education, AI is reshaping our daily lives and future opportunities.
Chapter 2
Intelligent Agents and Environments
Intelligent agents are crucial in understanding Artificial Intelligence. They can perceive their environment, act upon it, learn from experiences, and can be categorized based on their complexity and capabilities. The PEAS framework provides a method to define the components of an agent's environment and tasks, emphasizing the importance of rationality and autonomy in agent behavior.
Chapter 3
Search Algorithms and Problem Solving
Search algorithms are vital in artificial intelligence for solving problems by navigating a space of possible solutions. Uninformed strategies, like Breadth-First Search and Depth-First Search, operate without domain-specific knowledge, while informed strategies, such as A* and Greedy Best-First Search, utilize heuristics for efficient problem solving. The chapter emphasizes the need for effective heuristics and optimization techniques to tackle real-world problems effectively.
Chapter 4
Knowledge Representation and Reasoning
Knowledge Representation (KR) is crucial in AI, allowing for the encoding and manipulation of information. The chapter discusses logic-based representations, focusing on Propositional and First-Order Logic, as well as Ontologies and Semantic Networks. These methods help create intelligent systems capable of reasoning and making informed decisions in complex domains.
Chapter 5
Planning and Decision Making
Planning in AI focuses on generating sequences of actions to transition from an initial state to a desired goal state. Various planning systems, such as STRIPS and Goal Stack Planning, facilitate problem-solving in complex environments, while Markov Decision Processes (MDPs) deal with decision-making under uncertainty. These tools enable the design of intelligent agents capable of effective long-term goal achievement and rational behavior in both deterministic and uncertain contexts.
Chapter 6
Machine Learning Basics
Machine Learning (ML) is a crucial area within Artificial Intelligence that enables systems to learn from data without being explicitly programmed. It covers various learning paradigms, including supervised and unsupervised learning, the training and evaluation of models, as well as addressing the bias-variance trade-off. Mastering these principles is fundamental for creating effective machine learning systems that can generalize well to new data.
Chapter 7
Deep Learning and Neural Networks
Deep Learning is a transformative area of machine learning that employs artificial neural networks with multiple layers to identify complex data patterns. The progression from basic perceptron models to advanced structures like CNNs and RNNs showcases the capabilities of deep learning in various applications, including computer vision and natural language processing. Understanding these concepts equips individuals to leverage deep learning in developing sophisticated AI solutions.
Chapter 8
Natural Language Processing (NLP)
Natural Language Processing (NLP) is a crucial area of artificial intelligence focused on enabling machines to comprehend and generate human language. It encompasses various techniques such as text processing, tokenization, language modeling, and sentiment analysis, which are vital for creating applications like chatbots and sentiment analyzers. The chapter highlights how these components work together to improve machine understanding of natural language.
Chapter 9
Computer Vision
Computer Vision is a crucial branch of artificial intelligence focused on enabling machines to interpret visual information. It encompasses tasks such as image classification, object detection, and face recognition, utilizing techniques ranging from traditional feature extraction to modern deep learning methods. The versatility of computer vision finds applications in diverse fields such as transportation, healthcare, and retail.
Chapter 10
Reinforcement Learning
Reinforcement Learning (RL) is a machine learning paradigm where agents learn to make decisions through interaction with environments, receiving rewards or penalties. Key concepts include rewards, policies, and value functions essential for guiding the agent's behavior. Q-learning and deep Q-networks represent significant advancements in RL, enabling effective learning in complex tasks like robotics and gaming. Mastery of RL principles facilitates the development of autonomous systems that improve decision-making through experience.
Chapter 11
AI in Robotics
Artificial Intelligence plays a crucial role in enhancing the capabilities of robots, enabling them to perform tasks autonomously. Key areas of focus include path planning, perception through various sensors, and ensuring effective human-robot interaction. This integration of AI in robotics is vital for advancements across multiple industries, including manufacturing and healthcare.
Chapter 12
Ethics and Bias in AI
Ethical considerations and the potential for bias are critical as Artificial Intelligence systems become prevalent in society. Responsible AI development seeks fairness, transparency, and accountability while maximizing societal benefits and minimizing harms. It is essential to navigate the complex challenges of fairness, data privacy, and security to foster trust and social responsibility in AI systems.
Chapter 13
AI in Real-world Applications
Artificial Intelligence is revolutionizing various industries by automating tasks and improving decision-making and user experiences. The chapter focuses on notable applications in sectors like healthcare, finance, retail, and education, as well as foundational tools like recommendation systems and predictive analytics that leverage data for better outcomes.
Chapter 14
Tools and Technologies for AI Development
The chapter delves into the essential tools and technologies necessary for AI development, highlighting key Python libraries, interactive development environments, and outlining the AI development lifecycle. It emphasizes the significance of structured workflows in building AI systems to ensure effective outcomes. By leveraging powerful libraries such as TensorFlow, PyTorch, and Scikit-learn, along with platforms like Jupyter Notebooks and Google Colab, practitioners can accelerate AI innovation and deployment.
Chapter 15
Future of Artificial Intelligence
The chapter explores the future of artificial intelligence, contrasting narrow AI with artificial general intelligence (AGI), and discusses emerging trends such as explainable AI, edge AI, and automated machine learning. It outlines various career paths and research areas in AI, emphasizing the transformative potential of AI technologies and the importance of ethical considerations. It concludes by highlighting the need for adaptability in a rapidly evolving field.