AI-BASIC.AU1
Artificial Intelligence Basics
AI is changing everything. Time to change with it…our AI Basics Course makes sure you do.
- Practice in 9 Laboratorios prácticos — nothing to install
- 11 Lecciones interactivas y 97 topics mapped to the official exam objectives
Beginner A tu propio ritmo · 1 año de acceso
9 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
01 / Habilidades que obtendrás
What you will be able to do
Enroll in our AI Basics Course to demystify artificial intelligence and harness its power.
In this course, dive into machine learning, deep learning, NLP, and robotics through real-world case studies from companies like Uber and Facebook. Learn how to implement AI, avoid costly mistakes, and navigate ethical concerns while exploring AI’s impact on business and society.
From foundational concepts to hands-on labs, you’ll gain practical skills to evaluate AI solutions, deploy chatbots, and automate processes.
- Foundational AI Concepts: Understand core principles of AI, machine learning, deep learning, and NLP.
- AI Implementation Strategy: Learn best practices for deploying AI solutions using real-world case studies.
- Robotic Process Automation (RPA): Automate workflows and improve efficiency with RPA tools.
- Ethical AI & Risk Assessment: Identify ethical concerns, biases, and risks in AI systems.
- Natural Language Processing (NLP) Application: Build chatbots and voice recognition systems using NLP techniques.
- Future-Ready AI Forecasting: Analyze AI trends, societal impacts, and emerging technologies like autonomous systems.
Course Highlights
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11 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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9 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
11 Lecciones interactivas · 97 topics01 Introduction +
02 AI Foundations 11 topics · 1 Laboratorio en vivo +
- Alan Turing and the Turing Test
- Cybernetics
- The Origin Story
- Golden Age of AI
- AI Winter
- The Rise and Fall of Expert Systems
- Neural Networks and Deep Learning
- Technological Drivers of Modern AI
- Structure of AI
- Conclusion
- Key Takeaways
1 Laboratorio en vivo in this lesson — see the labs panel →
03 Data 9 topics · 1 Laboratorio en vivo +
- Data Basics
- Types of Data
- Big Data
- Databases and Other Tools
- Data Process
- Ethics and Governance
- More Data Terms and Concepts
- Conclusion
- Key Takeaways
1 Laboratorio en vivo in this lesson — see the labs panel →
04 Machine Learning 18 topics · 1 Laboratorio en vivo +
- What Is Machine Learning?
- Standard Deviation
- The Normal Distribution
- Bayes’ Theorem
- Correlation
- Feature Extraction
- What Can You Do with Machine Learning?
- The Machine Learning Process
- Applying Algorithms
- Common Types of Machine Learning Algorithms
- Naïve Bayes Classifier (Supervised Learning/Classification)
- K-Nearest Neighbor (Supervised Learning/Classification)
- Linear Regression (Supervised Learning/Regression)
- Decision Tree (Supervised Learning/Regression)
- Ensemble Modelling (Supervised Learning/Regression)
- K-Means Clustering (Unsupervised/Clustering)
- Conclusion
- Key Takeaways
1 Laboratorio en vivo in this lesson — see the labs panel →
05 Deep Learning 12 topics · 1 Laboratorio en vivo +
- Difference Between Deep Learning and Machine Learning
- So What Is Deep Learning Then?
- The Brain and Deep Learning
- Artificial Neural Networks (ANNs)
- Backpropagation
- The Various Neural Networks
- Deep Learning Applications
- Deep Learning Hardware
- When to Use Deep Learning?
- Drawbacks with Deep Learning
- Conclusion
- Key Takeaways
1 Laboratorio en vivo in this lesson — see the labs panel →
06 Robotic Process Automation (RPA) 8 topics · 1 Laboratorio en vivo +
- What Is RPA?
- Pros and Cons of RPA
- What Can You Expect from RPA?
- How to Implement RPA
- RPA and AI
- RPA in the Real World
- Conclusion
- Key Takeaways
1 Laboratorio en vivo in this lesson — see the labs panel →
07 Natural Language Processing (NLP) 10 topics · 1 Laboratorio en vivo +
- The Challenges of NLP
- Understanding How AI Translates Language
- Voice Recognition
- NLP in the Real World
- Voice Commerce
- Virtual Assistants
- Chatbots
- Future of NLP
- Conclusion
- Key Takeaways
1 Laboratorio en vivo in this lesson — see the labs panel →
08 Physical Robots 10 topics · 1 Laboratorio en vivo +
- What Is a Robot?
- Industrial and Commercial Robots
- Robots in the Real World
- Humanoid and Consumer Robots
- The Three Laws of Robotics
- Cybersecurity and Robots
- Programming Robots for AI
- The Future of Robots
- Conclusion
- Key Takeaways
1 Laboratorio en vivo in this lesson — see the labs panel →
09 Implementation of AI 8 topics · 1 Laboratorio en vivo +
- Approaches to Implementing AI
- The Steps for AI Implementation
- Identify a Problem to Solve
- Forming the Team
- The Right Tools and Platforms
- Deploy and Monitor the AI System
- Conclusion
- Key Takeaways
1 Laboratorio en vivo in this lesson — see the labs panel →
10 The Future of AI 10 topics · 1 Laboratorio en vivo +
- Autonomous Cars
- US vs. China
- Technological Unemployment
- The Weaponization of AI
- Drug Discovery
- Government
- AGI (Artificial General Intelligence)
- Social Good
- Conclusion
- Key Takeaways
1 Laboratorio en vivo in this lesson — see the labs panel →
11 Appendix 1 topics +
- What Is Artificial Intelligence? Complete Beginner Guide + Career Options
Laboratorios prácticos Our edge
9 Laboratorio en vivos- Exploring AI History and Key Concepts
- Understanding and Managing Data Types Effectively
- Reviewing Machine Learning Concepts
- Exploring Deep Learning Concepts and Challenges
- Enhancing Operational Efficiency through Robotic Process Automation
- Revising NLP Concepts
- Transforming Work with Robotics and AI
- Deploying AI Systems
- Charting AI’s Transformation
03 / Preguntas frecuentes
Preguntas antes de empezar
How can I learn AI in depth as a complete beginner?+
Start with an Introduction to Artificial Intelligence Course to build a strong foundation in key concepts like machine learning, deep learning, and NLP. Follow a structured learning path:
- Step 1: Learn Python (the most common AI programming language).
- Step 2: Study math fundamentals (statistics, linear algebra, calculus).
- Step 3: Take beginner-friendly AI/ML courses (like this one) with hands-on projects.
- Step 4: Practice with real datasets on platforms like Kaggle.
- Step 5: Explore advanced topics (neural networks, computer vision, etc.) through specialized courses.
This course is designed for absolute beginners, making AI easy to grasp without a technical background.
Can I learn AI myself?+
Yes! Many AI professionals are self-taught. The key is:
- Structured Learning: Follow a well-organized course (like this AI course for beginners) to avoid confusion.
- Hands-on Practice: Work on small projects (e.g., chatbots, prediction models) to reinforce learning.
- Community & Resources: Use free tools (TensorFlow, PyTorch), forums (Stack Overflow), and AI communities for support.
This course provides real-world case studies, interactive labs, and step-by-step guidance, making self-learning effective and engaging.
Keep going
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Learn AI, Lead Tomorrow
Master AI basics fast, automate smarter, and stand out because the future belongs to those who adapt first.
- 1 año de acceso completo
- 9 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito