Introducción a la Inteligencia Artificial
This course is part of Inteligencia Artificial y Robótica.
This comprehensive course introduces students to the rapidly growing field of Artificial Intelligence (AI) and its increasing importance in both personal and professional life. The curriculum explores the fundamental question of whether it's possible to build a machine or robot as intelligent as a human being—a core challenge that AI seeks to address by creating machines capable of perceiving their environment and successfully performing tasks. Students will discover how AI enables communication with phones and computers as if they were human, from simple voice commands for playing music or controlling home devices to more complex applications like predicting customer retention or analyzing social media for customer satisfaction insights. The course covers theoretical foundations of AI and its primary branches, including Machine Learning, Deep Learning, Robotics, Natural Language Processing, and Expert Systems. Participants will apply these concepts by creating models such as Natural Language Processing, Classification Trees, and Bayesian Classifiers. Additionally, students will learn to use the RapidMiner data science platform for designing and testing AI models, providing them with practical skills for implementing AI solutions across various domains.
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What you'll learn
Understand concepts related to Artificial Intelligence and its branches
Design and apply prediction models based on Machine Learning for data-driven decision making
Create and implement Natural Language Processing models
Learn the evolution and applications of Artificial Intelligence
Master techniques for Natural Language Processing and their practical applications
Apply Decision Tree classification methods for predictive analytics
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
This introductory course provides a comprehensive overview of Artificial Intelligence and its applications in modern technology. Students explore the evolution of AI from its conceptual beginnings to its current implementations across various domains. The curriculum covers theoretical foundations and practical applications of key AI branches including Machine Learning, Deep Learning, Robotics, Natural Language Processing, and Expert Systems. Through four structured modules, participants first gain an understanding of core AI concepts, learning about its evolution, key definitions, applications, and successful use cases. The second module focuses on Natural Language Processing (NLP), exploring techniques that enable machines to understand, interpret, and generate human language. Students learn classification methods in the third module, with emphasis on Decision Trees as powerful predictive tools. The final module covers Bayesian Classification techniques and their applications. Throughout the course, theoretical concepts are reinforced with practical exercises using RapidMiner, a data science platform that allows students to design and test AI models without extensive programming knowledge. By the end of the course, participants will understand AI's capabilities and limitations while gaining hands-on experience with fundamental AI techniques.
La Inteligencia Artificial
Module 1
Procesamiento de Lenguaje Natural
Module 2
Árboles de Clasificación
Module 3
Clasificador Bayesiano
Module 4
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Inteligencia Artificial y Robótica
Payment options
Financial Aid
Instructor
Dr. (PhD.) at Anáhuac Universities
Dr. Román Alberto Zamarripa Franco is a professor of programming, research, and business at IEST Anáhuac Tampico; and he teaches Big Data and Data Mining at Universidad Virtual Anáhuac.
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