OUR EXPERT'S VISION
LA VISIÓN DE N
En un entorno donde los datos son el nuevo lenguaje de la empresa, comprender y visualizarlos correctamente se ha convertido en una competencia esencial. Este curso nace para ayudar a profesionales y organizaciones a transformar la información dispersa en conocimiento visual y accionable.
A través de un recorrido estructurado y práctico, los participantes aprenden a interpretar, modelar y presentar datos de forma eficaz, desarrollando una mentalidad analítica aplicada al día a día.
La formación está diseñada bajo el modelo Sabio Valley de “formación concentrada”: intensiva, aplicable y acompañada por expertos que aseguran la comprensión real de cada paso.
Incluye una tutoría personalizada de 20 minutos por alumno y sugiere una hora adicional de mentoría adaptada con experto mentor para quienes deseen llevar su dashboard a un nivel profesional.
Title
Encabezado 6
Hours CLASS SESSIONS:
WEEKS:
Minimum TOTAL hours of COMPLETION of the course:
AVAILABILITY:
CURRENT PRICE (€):
NIEX´s value:
AGE (minimum recommended):
ESTIMATED NEXT DATE OF CELEBRATION:
Can the Course be FREE?:
8
From 2 to 4
24
IN PREPARATION
350
995
+18
Starting November 15th
YES, ASK US HOW
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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