AI-FUNDA.KZ1 ISBN: 979-8-90059-162-9
Introduction to Artificial Intelligence
Master AI with this comprehensive Introduction to Artificial Intelligence training, featuring 150 quizzes and 137 essential flashcards.
What you will be able to do
- AI Problem Solving: Mastery of state-space representation, search strategies, and constraint satisfaction to build efficient, goal-oriented intelligent agents.
- Machine Learning Engineering: Proficiency in the full ML lifecycle, including data preparation, feature engineering, and deploying models using modern MLOps practices.
- Generative AI & NLP: Advanced capability in utilizing transformers, large language models, and prompt engineering to create scalable, intelligent text-based applications.
- Responsible AI Governance: Deep understanding of bias mitigation, transparency, and regulatory standards required to deploy secure and ethical AI systems.
Intermediate A tu propio ritmo · 1 año de acceso
26 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
01 / About
Acerca de este curso
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No se requiere tarjeta de crédito
This Introduction to Artificial Intelligence training provides a rigorous, hands-on path for beginners to master complex machine learning concepts. You will navigate 13 comprehensive chapters covering everything from intelligent agents and neural networks to generative AI and MLOps. We prioritize practical skill building, utilizing 150 practice quizzes and 137 flashcards to reinforce your technical retention. While the breadth of topics is vast, the trade-off is the intense focus required to grasp deep learning architectures. This course bridges the gap between theoretical foundations and real-world deployment, ensuring you are prepared for professional certification and the evolving demands of the modern AI-driven workforce.
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
13 Lecciones interactivas · 68 topics01 Preface +
02 Foundations of Artificial Intelligence 5 topics · 1 Laboratorio en vivo +
- Understanding Artificial Intelligence (AI)
- History and Evolution of Artificial Intelligence
- Types and Approaches to Artificial Intelligence
- Computational Foundations of AI
- AI Applications, Careers, and Professional Skills
1 Laboratorio en vivo in this lesson — see the labs panel →
03 Intelligent Agents and AI Problem Solving 5 topics · 1 Laboratorio en vivo +
- Foundations of Intelligent Agents
- Designing Agents with the PEAS Framework
- AI Environment Characteristics
- Problem Formulation and State-Space Representation
- Agent Architectures and Behaviors
1 Laboratorio en vivo in this lesson — see the labs panel →
04 Knowledge Representation, Reasoning, and Uncertainty 5 topics · 3 Laboratorio en vivo +
- Representing Knowledge in AI Systems
- Logic-Based Reasoning
- Expert Systems and Rule-Based AI
- Reasoning Under Uncertainty
- Sequential and Approximate Reasoning
3 Laboratorio en vivo in this lesson — see the labs panel →
05 Search, Optimization, Planning, and Decision Making 5 topics · 5 Laboratorio en vivo +
- Foundations of AI Search
- Uninformed Search Strategies
- Informed and Local Search
- Optimization and Constraint Satisfaction
- Planning, Games, and Sequential Decisions
5 Laboratorio en vivo in this lesson — see the labs panel →
06 Data and Machine Learning Foundations 6 topics · 2 Laboratorio en vivo +
- Data, Mathematics, and Statistics for Machine Learning
- Machine Learning Concepts and Paradigms
- The Machine Learning Workflow
- Data Preparation and Feature Engineering
- Foundational Machine Learning Algorithms
- Model Evaluation and Improvement
2 Laboratorio en vivo in this lesson — see the labs panel →
07 Neural Networks, Deep Learning, and Reinforcement Learning 5 topics · 5 Laboratorio en vivo +
- Neural Network Fundamentals
- Training Deep Learning Models
- Deep Learning Architectures
- Transfer Learning and Generative Deep Learning
- Reinforcement Learning
5 Laboratorio en vivo in this lesson — see the labs panel →
08 Computer Vision, Speech, Robotics, and Multimodal AI 5 topics · 2 Laboratorio en vivo +
- Foundations of Machine Perception
- Computer Vision
- Speech and Audio AI
- Robotics and Autonomous Systems
- Multimodal and Edge AI
2 Laboratorio en vivo in this lesson — see the labs panel →
09 Natural Language Processing and Generative AI 6 topics · 2 Laboratorio en vivo +
- Natural Language Processing Foundations
- Transformers, Foundation Models, and Large Language Models
- Generative AI Models and Capabilities
- Prompt Engineering
- Retrieval-Augmented Generation and AI Agents
- Evaluating Generative AI Solutions
2 Laboratorio en vivo in this lesson — see the labs panel →
10 Responsible, Ethical, Secure, and Governed AI 6 topics · 2 Laboratorio en vivo +
- Responsible AI Principles and Social Impact
- Bias, Fairness, and Inclusion
- Transparency, Explainability, and Accountability
- AI Privacy, Security, and Safety
- AI Governance, Regulation, and Standards
- Responsible Use of Generative AI
2 Laboratorio en vivo in this lesson — see the labs panel →
11 AI Engineering Lifecycle, Tools, and MLOps 6 topics · 1 Laboratorio en vivo +
- Identifying and Defining AI Opportunities
- The AI Development Lifecycle
- AI Programming Tools and Platforms
- MLOps and AI Operations
- Monitoring, Testing, and Maintenance
- Project Management and Documentation
1 Laboratorio en vivo in this lesson — see the labs panel →
12 AI Applications Across Industries and the Workforce 8 topics · 1 Laboratorio en vivo +
- AI in Healthcare and Life Sciences
- AI in Business, Finance, and Retail
- AI in Manufacturing, Supply Chain, and Robotics
- AI in Transportation and Smart Mobility
- AI in Education and Research
- AI in Cybersecurity and Public Services
- AI in Media and Creative Industries
- AI in Workplace Productivity and Automation
1 Laboratorio en vivo in this lesson — see the labs panel →
13 Future Horizons and Capstone Project 6 topics · 1 Laboratorio en vivo +
- Emerging AI Trends
- Human-AI Collaboration and the Future of Work
- Academic, Career, and Portfolio Pathways
- Capstone Planning and Requirements
- Capstone Design and Prototype Development
- Capstone Evaluation, Presentation, and Reflection
1 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
26 Laboratorio en vivos- Understanding How AI Systems Work
- Designing an Intelligent Agent
- Calculating Probability Using a Bayesian Network
- Calculating Posterior Probability Using Bayes’ Theorem
- Calculating Probability of an Event
- Performing Iterative-Deepening Search (IDS)
- Performing Breadth-First Search (BFS)
- Performing Depth-First Search (DFS)
- Performing Uniform-Cost Search (UCS)
- Performing Greedy Best-First Search and A* Search
- Calculating Descriptive Statistics for a Dataset
- Calculating a Linear Regression Prediction
- Calculating the Artificial Neuron Output
- Calculating Batches per Epoch
- Calculating Mean Absolute Error (MAE)
- Calculating Mean Squared Error (MSE)
- Calculating the Updated Q-Value
- Testing ASR in Customer Support
- Analyzing an Image with Multimodal AI
- Create Multimodal Learning Content with AI
- Designing and Refining Effective Prompts
- Evaluating an AI System for Responsible AI and Risk
- Assessing Generative AI Content for Responsible Use
- Planning an AI Solution for a Cafe
- Understanding AI in Everyday Life
- AI Engineer Telephonic Interview
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
Is this Introduction to Artificial Intelligence course suitable for absolute beginners?+
Yes, the curriculum starts with foundational concepts before moving into advanced neural networks, making it accessible for beginners with basic technical aptitude.
How does this course prepare me for certification?+
With 150 practice quizzes and 137 flashcards, this course provides the repetitive, high-intensity study environment necessary to pass rigorous certification exams.
What is the primary trade-off in this curriculum?+
The course covers a massive breadth of topics; the trade-off is that you must dedicate significant time to self-study to master the complex mathematical foundations.
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Build Your AI Skills
Develop practical skills in machine learning, generative AI, NLP, MLOps, and responsible AI through hands-on learning and practice.
- 1 año de acceso completo
- 26 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito