Art and Science of Machine Learning en Español from Coursera

What's inside

Syllabus

Introducción

Le damos la bienvenida a The Art and Science of Machine Learning. En este curso, se abordan las habilidades básicas de intuición, buen criterio y experimentación del AA necesarias para ajustar mejor y optimizar modelos de AA a fin de lograr el mejor rendimiento. Aprenderá a generalizar su modelo usando técnicas de regularización y descubrirá los efectos de los hiperparámetros, como el tamaño del lote y la tasa de aprendizaje, en el rendimiento del modelo. Analizaremos algunos de los algoritmos de optimización de los modelos más comunes y le mostraremos cómo especificar un método de optimización en su código de TensorFlow.

El arte del AA

En este módulo, aprenderá a ajustar el tamaño del lote y la tasa de aprendizaje para mejorar el rendimiento del modelo, optimizar su modelo y aplicar los conceptos en el código de TensorFlow.

Ajuste de hiperparámetros

En este módulo, aprenderá a diferenciar entre parámetros e hiperparámetros. Luego, veremos el enfoque tradicional de búsqueda por cuadrícula y aprenderemos a ir más allá mediante algoritmos más inteligentes. Por último, verá cómo Cloud ML Engine facilita la automatización del ajuste de hiperparámetros.

Una pizca de ciencia

En este módulo, comenzaremos a presentar la ciencia junto con el arte del aprendizaje automático. Primero, hablaremos sobre cómo realizar una regularización para lograr dispersión y, de este modo, conseguir modelos más simples y concisos. Luego, abordaremos la regresión logística y aprenderemos a determinar el rendimiento.

La ciencia de las redes neuronales

En este módulo, profundizaremos en la ciencia, específicamente en las redes neuronales.

Incorporaciones

En este módulo, aprenderá a usar incorporaciones para administrar los datos dispersos, a fin de que los modelos de aprendizaje automático que usan ese tipo de datos consuman menos memoria y se entrenen más rápido. Las incorporaciones también son una forma de reducir la dimensionalidad. Esto hace que los modelos sean más simples y generalizables.

Resumen

Resumen de los puntos clave de aprendizaje del curso.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Enfatiza la intuición, el juicio y la experimentación, que son habilidades esenciales para los profesionales de ML

Se centra en ajustar y optimizar los modelos de ML para obtener el máximo rendimiento

Profundiza en las técnicas de regularización para evitar el sobreajuste y mejorar la generalización

Enseña a utilizar algoritmos de optimización de modelos comunes en TensorFlow

Abarca la optimización del tamaño del lote y la tasa de aprendizaje para mejorar el rendimiento del modelo

Requiere un conocimiento previo de ML y TensorFlow

Reviews summary

Valuable machine learning course in spanish

The "Art and Science of Machine Learning en Español" is a well-received course that teaches the basics of machine learning in Spanish. Students appreciate the course's clear and concise explanations, as well as its focus on practical applications. They also find the course to be challenging but rewarding, and they report that they have learned a great deal from it. Overall, this course is a valuable resource for anyone who wants to learn more about machine learning in Spanish.

Excellent course

"Excelente"

"Excelente!!!!"

"Fue muy didactico, directo y me senti muy comodo al hacerlo. Es un gran curso"

Not in Spanish

"sigue siendo venta de su propia plataforma. Poca ejercitacion real, poco detalle del codigo fuente. y no esta en "español" solo esta subtitulado, y todo el material en ingles."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Art and Science of Machine Learning en Español with these activities:

Seguir tutoriales sobre redes neuronales

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Fortalecerá sus conocimientos sobre las redes neuronales y sus aplicaciones, preparándolo para el módulo dedicado del curso.

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Siga tutoriales sobre redes neuronales convolucionales (CNN)
Explore las redes neuronales recurrentes (RNN)
Implemente una red neuronal simple en TensorFlow

Organizar notas y materiales del curso

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Mejorará la retención y la revisión al mantener organizadas y accesibles las notas, las asignaciones y otros materiales del curso.

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Crear un sistema para organizar las notas, como carpetas digitales o un cuaderno
Revisar regularmente las notas y los materiales para reforzar el aprendizaje

Contribuir a un proyecto de código abierto relacionado con la optimización de modelos

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Le permitirá aplicar sus conocimientos prácticos, colaborar con otros y mantenerse actualizado en las tendencias de la industria.

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Identificar un proyecto de código abierto relevante
Revisar la documentación y el código fuente
Hacer una contribución, como corregir errores o agregar nuevas funciones

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Career center

Learners who complete Art and Science of Machine Learning en Español will develop knowledge and skills that may be useful to these careers:

Data Scientist

A cornerstone of what a Data Scientist does is apply artificial intelligence to data in such a way that value is derived from it. As an integral part of that process, Data Scientists often have to optimize and tune machine learning models so that they perform well. This course from Google Cloud will give you the skills and insights to be able to develop your problem-solving capabilities in the domain of artificial intelligence and machine learning.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

At its core, a Machine Learning Engineer takes a holistic approach to constructing, evaluating, and deploying machine learning models. This includes learning how to avoid overfitting and underfitting, as well as understanding how to best tune hyperparameters. As such, this course can be extremely beneficial as it covers the art and science behind these concepts in detail.

See salaries and explore the career path for Machine Learning Engineer

Software Developer

Software Developers are responsible for bringing the ideas of engineers to life through the writing of code. To that end, it's helpful to have a fundamental understanding of how the algorithms that produce predictions in machine learning models work. The science of machine learning, which this course covers in depth, will be of great benefit on your journey to becoming a well-rounded Software Developer.

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Data Analyst

Data Analysts are responsible for surfacing insights from data. To that end, proficiency with machine learning models is incredibly valuable. This course will introduce you to the theory and practice of machine learning, which can be especially useful when working with large datasets.

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Business Analyst

Business Analysts are experts in using data to uncover business trends and opportunities. To that end, being able to collect, clean, and analyze data with machine learning models is crucial. This course will provide you with a solid foundation in the science and art of machine learning, which will be a valuable tool to have in your arsenal.

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Product Manager

Product Managers are tasked with managing the development of products from start to finish. In this day and age, many of those products incorporate machine learning in some form. This course will help you understand the basics of machine learning, which will help you bridge the gap between yourself and your engineers and ensure that everyone is working cohesively.

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Project Manager

Project Managers are responsible for planning, coordinating, and executing projects. With many projects incorporating machine learning and artificial intelligence, being able to understand the basics of the field from a scientific perspective is becoming more important. This course can help you develop that understanding and will empower you to manage your projects more effectively.

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Systems Engineer

Systems Engineers are responsible for designing, developing, and maintaining complex systems. Given how much machine learning and artificial intelligence is pervading our technological landscape, a basic understanding of these technologies is becoming increasingly important for Systems Engineers. This course will help you build that foundation.

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Network Engineer

Network Engineers design, develop, and maintain computer networks. These days, those networks are being increasingly reliant upon machine learning and artificial intelligence for tasks like automating network management and security. This course will give you an introduction to the science and art of machine learning, which will help you excel in the fast-paced environment of network engineering.

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Database Administrator

Database Administrators are responsible for the installation, configuration, and maintenance of database management systems. As organizations continue to collect and store more data, machine learning is increasingly being used for database management tasks, including data cleaning, indexing, and query optimization. Familiarizing yourself with machine learning through this course will help you be more effective in your work.

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Security Analyst

Security Analysts are tasked with protecting computer networks and systems from security breaches. With many security threats becoming more sophisticated, machine learning is being used to develop more effective ways to detect and prevent them. This course will give you a basic understanding of how machine learning can be used for cybersecurity, which will help you stay ahead of the curve.

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IT Manager

IT Managers plan, coordinate, and direct the activities of an organization's IT department. With machine learning becoming increasingly important in various aspects of IT, including data analysis, network management, and security, having a fundamental understanding of machine learning is becoming increasingly important for IT Managers. This course will give you a broad overview of the field.

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Hardware Engineer

Hardware Engineers research, design, develop, and test computer hardware. With the advent of specialized hardware for machine learning and artificial intelligence, it has become increasingly important for Hardware Engineers to have at least a basic understanding of these domains. This course can help provide that understanding.

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Computer Systems Analyst

Computer Systems Analysts study an organization's computer systems and procedures to help it operate more efficiently and effectively. As organizations increasingly rely on machine learning and artificial intelligence to improve their operations, Systems Analysts who have an understanding of these technologies will be more valuable to their organizations. This course can be a good starting point for developing that understanding.

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Software Tester

Software Testers evaluate the functionality of software to ensure that it meets user requirements. With machine learning becoming more prevalent in software, Software Testers need to have an understanding of how machine learning models work, as well as how to test them effectively. This course will give you a basic introduction to machine learning, which will be helpful in your role as a Software Tester.

See salaries and explore the career path for Software Tester