AI: FUNDAMENTALS OF ARTIFICIAL INTELLIGENCE
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8
From 2 to 4
24
IN PREPARATION
199
995
+18
Starting November 15th
YES, ASK US HOW
OUR EXPERT'S VISION
Angel Luis Aldana
This basic artificial intelligence (AI) course will help you understand the main concepts and fundamentals of this booming discipline. It is designed to teach the key concepts, methodologies, and applications of AI in various areas. This course is ideal for both beginners and those with basic programming knowledge who want to delve into the world of AI.
The “AI: FUNDAMENTALS OF ARTIFICIAL INTELLIGENCE” COURSE will provide you with a fundamental understanding of the algorithms and technologies that enable machines to “think” and make decisions in a similar way to humans. It will introduce you to a variety of techniques, from machine learning to neural networks, with the aim of equipping you to develop AI-based solutions that can be applied in sectors such as robotics, healthcare, data analytics, and more.
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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