INFORMATION
INTELIGENCIA ARTIFICIAL PARA PRESUPUESTOS DE CONSTRUCCIÓN Y REFORMAS
Técnicas y herramientas de ia para que los equipos de administración y comercial de empresas de obras y reformas elaboren presupuestos más precisos, rápidos y profesionales.
If the course has the status of IMMINENT ANNOUNCEMENT or IN PREPARATION, you can secure your place with a discount.
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"Inteligencia Artificial para Presupuestos de Construcción y Reformas" es un curso práctico dirigido al personal de administración y el equipo comercial de empresas del sector de la construcción y las reformas. En lenguaje directo y orientado a resultados, el participante aprende a utilizar herramientas de IA —especialmente Claude y ChatGPT— para acelerar y mejorar el proceso de elaboración de presupuestos. El curso combina fundamentos conceptuales con aplicación inmediata: desde entender qué puede y qué no puede hacer la IA, hasta crear plantillas de prompts conectadas con bases de datos de precios propias de la empresa, pasando por casos prácticos reales de reformas integrales, locales comerciales y rehabilitaciones de fachadas. El objetivo final es que cada participante salga del curso con un sistema propio de generación de presupuestos apoyado en IA, reduciendo el tiempo de elaboración y aumentando la calidad y profesionalidad del documento entregado al cliente.
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Encabezado 6
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Can the Course be FREE?:
8
From 2 to 4
24
IN PREPARATION
350
995
+18
Starting November 15th
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GUIDANCE AGENDA FOR COURSE CONTENTS
1. Introduction to Artificial Intelligence
Definition and objectives of AI.
History of AI and its evolution.
Differences between weak and strong AI.
Applications of AI in daily life (virtual assistants, recommendation systems, etc.).
2. Basic Concepts of Machine Learning
Machine Learning : concept and types (supervised, unsupervised and reinforcement).
Difference between Machine Learning and Traditional Programming.
Machine Learning project life cycle.
Main machine learning algorithms (linear regression, k-means, decision trees).
3. Artificial Neural Networks
What are neural networks and how do they work?
Concepts of neurons, layers and weights.
Practical examples of neural networks in action.
Introduction to Deep Neural Networks (Deep Learning).
4. Natural Language Processing (NLP)
What is NLP and how does it help machines understand human language?
Common techniques such as sentiment analysis and machine translation.
NLP applications (chatbots, virtual assistants, etc.).
5. Computer Vision
What is computer vision?
Image recognition and video processing.
Convolutional neural networks (CNN) for vision.
6. Search and Problem Solving Algorithms
Graph search (BFS, DFS).
Heuristic algorithms like A*.
Problem solving with AI.
7. Ethics and Responsibility in AI
Social impact of AI.
Ethical and moral challenges (biases in algorithms, privacy, automated decision-making).
Regulation and control of AI development.
8. Tools and Programming Languages for AI
CHATGPT and similar.
Introduction to common programming languages in AI: Python (libraries such as TensorFlow, Keras, Scikit-learn).
Development platforms and tools such as Jupyter Notebooks, Google Colab.
Examples of small projects to put the concepts learned into practice.
At the moment you finish this course, you will receive a certificate of completion:
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