Completion

CERTIFICATE

Fundamentos de la Inteligencia Artificial

Name: Fundamentos de la Inteligencia Artificial
Price: 3354 INR
Rating: 3.8 (17 reviews)

This course is part of Inteligencia Artificial y Robótica.

Course Cost

₹ 3,354

Beginner

Skill Level

4 Weeks

Self-paced lessons

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.

3.8

27,309 Enrolled

Roman Alberto Zamarripa Franco

Spanish

3.8

27,309 Enrolled

Spanish

Roman Alberto Zamarripa Franco

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

Implement Bayesian classifiers for probabilistic data analysis

Use the RapidMiner platform for designing and testing AI models

Skills you'll gain

Artificial Intelligence

Machine Learning

Deep Learning

Natural Language Processing

Expert Systems

Decision Tree Learning

Bayesian Classification

RapidMiner

Data Science

Customer Satisfaction

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

Este módulo introduce los conceptos fundamentales de la Inteligencia Artificial, explorando su evolución histórica, definiciones clave, y aplicaciones prácticas en diversos campos. Los estudiantes comprenderán qué es la IA, cómo ha evolucionado a lo largo del tiempo, los diferentes tipos de enfoques y modelos utilizados en la IA, y analizarán casos de éxito en su implementación. Este módulo proporciona las bases conceptuales necesarias para entender cómo la IA está transformando nuestra vida personal y laboral.

Skills you'll gain

Fundamentos de IA

Evolución de la IA

Aplicaciones Prácticas

Procesamiento de Lenguaje Natural

Module 2

Este módulo se centra en una de las ramas más importantes de la Inteligencia Artificial: el Procesamiento de Lenguaje Natural (PLN). Los estudiantes aprenderán los conceptos básicos del PLN, las diferentes técnicas y algoritmos utilizados para analizar y procesar el lenguaje humano, y explorarán diversas aplicaciones prácticas como análisis de sentimientos, chatbots, y sistemas de recomendación. El módulo incluye ejercicios prácticos donde los estudiantes podrán aplicar las técnicas aprendidas utilizando herramientas como RapidMiner.

Skills you'll gain

Análisis Textual

Algoritmos PLN

Análisis de Sentimientos

Árboles de Clasificación

Module 3

Este módulo aborda los Árboles de Clasificación o Decisión, una técnica fundamental del Aprendizaje Automático. Los estudiantes comprenderán cómo funcionan los árboles de decisión, cómo se construyen a partir de datos, y cómo se utilizan para realizar predicciones y clasificaciones. Se explorarán diversas aplicaciones prácticas de esta técnica en campos como marketing, finanzas, y atención al cliente. El módulo incluye ejercicios prácticos donde los estudiantes diseñarán y evaluarán sus propios modelos de árboles de clasificación.

Skills you'll gain

Modelos Predictivos

Toma de Decisiones

Clasificación de Datos

Clasificador Bayesiano

Module 4

Este módulo explora el Clasificador Bayesiano, un método probabilístico basado en el teorema de Bayes utilizado ampliamente en Inteligencia Artificial. Los estudiantes aprenderán los fundamentos teóricos de esta técnica, cómo implementarla para resolver problemas de clasificación, y analizarán sus aplicaciones en filtrado de spam, diagnóstico médico, y sistemas de recomendación, entre otros. El módulo incluye ejercicios prácticos donde los estudiantes aplicarán el clasificador bayesiano a problemas reales, utilizando herramientas como RapidMiner para diseñar y evaluar sus modelos.

Skills you'll gain

Análisis Probabilístico

Clasificación Bayesiana

Filtrado de Información

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

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Courses

Roman Alberto Zamarripa Franco

Dr. Zamarripa teaches Big Data and research at Anáhuac, with 24 years of experience and a focus on educational tech, networks, and programming.

Beginner

Skill Level

4 Weeks

Self-paced lessons

Course Cost

₹ 3,354

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.

What is Artificial Intelligence?

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines programmed to think and learn like humans. It encompasses various technologies that enable computers to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. AI systems are designed to analyze their environment, learn from experience, adapt to new inputs, and perform human-like tasks with increasing autonomy and sophistication.

How is AI being used in everyday life?

AI is increasingly integrated into everyday life through virtual assistants (like Siri and Alexa), recommendation systems on streaming platforms and e-commerce sites, smart home devices, navigation apps that predict traffic patterns, email filtering that detects spam, facial recognition for phone unlocking, predictive text when messaging, and automated customer service chatbots. These applications demonstrate how AI has become seamlessly embedded in daily activities, often without users consciously recognizing its presence.

Do I need programming knowledge to take this course?

No, this course is designed for beginners and requires only basic computer usage knowledge. While programming skills can be helpful, they are not a prerequisite. The course focuses on conceptual understanding of AI and its applications, and practical exercises are conducted using RapidMiner, a visual workflow designer that allows you to create AI models without extensive coding. The emphasis is on understanding AI principles and applications rather than implementation details.

What career opportunities exist in the field of AI?

Career opportunities in AI span multiple industries including tech companies, healthcare, finance, manufacturing, retail, transportation, and education. Roles include AI specialists, machine learning engineers, data scientists, NLP specialists, robotics engineers, business intelligence analysts, AI ethics consultants, and AI product managers. As organizations increasingly adopt AI technologies, professionals with AI knowledge are in high demand, even in roles that aren't traditionally technical but require understanding AI capabilities and limitations.

How can businesses benefit from the AI concepts taught in this course?

Businesses can benefit from AI concepts by implementing customer sentiment analysis to understand satisfaction levels, using predictive models to forecast customer behavior and prevent churn, deploying natural language processing for automated customer service, applying decision trees for data-driven decision making, and utilizing classification models to segment customers for targeted marketing. These applications can lead to improved operational efficiency, enhanced customer experiences, reduced costs, and data-informed strategic planning, providing competitive advantages in today's technology-driven marketplace.