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Leire Ahedo
Es un curso práctico y efectivo para aprender a generar modelos de regresión (Machine Learning) con PySpark en un entorno de Big Data. Te enseñaremos desde cero los fundamentos de Spark y MLlib, y acabarás desarrollando avanzados modelos de regresión en PySpark para predecir el precio de las viviendas o el número de bicis que se alquilarán por horas.
Register for this course and see more details by visiting:
OpenCourser.com/course/0cu81f/machine
What's inside
Syllabus
Machine Learning y Regresión con PySpark. Guía paso a paso
En este curso se aprenderá a generar modelos de Machine Learning con Spark (MLlib) en Databricks
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explora los fundamentos de Spark y MLlib, que conforman la base para la ciencia de datos
Proporciona una guía paso a paso sobre la creación de modelos de Machine Learning con PySpark, lo que lo hace accesible para principiantes
Se enfoca en la aplicación práctica, con el objetivo de que los estudiantes desarrollen modelos de regresión avanzados para resolver problemas del mundo real
Está dirigido a aquellos que buscan desarrollar habilidades en el uso de PySpark para el análisis de Big Data y el Machine Learning
Save this course
Save Machine Learning y Regresión con PySpark. Guía paso a paso to your list so you can find it easily later:
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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 Machine Learning y Regresión con PySpark. Guía paso a paso with these
activities:
Revise Spark Fundamental Concepts
Show steps
Revisit key concepts in Spark, including RDDs, transformations, and actions, to strengthen your understanding before diving into regression models.
Browse courses on
Apache Spark
Show steps
-
Review the Spark documentation or online tutorials on core concepts. (e.g. RDDs, transformations, actions)
-
Work through simple Spark examples to refresh your hands-on experience.
Explore Spark Machine Learning Library (MLlib) Tutorials
Show steps
Supplement your course material by following guided tutorials on Spark MLlib, focusing on regression algorithms and techniques to enhance your practical skills.
Browse courses on
Spark MLlib
Show steps
-
Search for tutorials on the official Spark MLlib website or reputable online platforms.
-
Select tutorials covering regression topics relevant to the course, such as linear regression or decision trees.
-
Follow the tutorials step-by-step, implementing the regression algorithms in PySpark.
Participate in Online Discussion Forums
Show steps
Engage in discussions with fellow learners by participating in online forums or chat groups. Exchanging ideas, asking questions, and providing support can enhance your understanding and foster a sense of community.
Show steps
-
Identify relevant online forums or chat groups related to Spark and regression.
-
Actively participate in discussions by sharing your insights, asking questions, and providing feedback to others.
-
Be respectful and mindful of diverse perspectives and opinions.
Three other activities
Expand to see all activities and additional details
Show all six activities
Assist Peers in Understanding Regression Concepts
Show steps
Deepen your own understanding by explaining regression concepts to others. This will reinforce your knowledge and allow you to identify areas where you need further improvement.
Show steps
-
Identify opportunities to assist peers who may be struggling with regression concepts.
-
Provide clear and concise explanations, using examples and analogies to make concepts relatable.
-
Encourage peers to ask questions and engage in discussions.
Develop a Regression Model for a Real-World Dataset
Show steps
Apply your regression skills to a real-world dataset, implementing a regression model in PySpark to solve a specific problem or make predictions. This will demonstrate your ability to apply your knowledge in a practical context.
Browse courses on
Data Analysis
Show steps
-
Identify a dataset that aligns with your interests or a specific industry.
-
Load and explore the dataset in PySpark, identifying potential features and target variables.
-
Select appropriate regression algorithms and tune them using cross-validation.
-
Evaluate the performance of your model and present your findings in a clear and concise manner.
Contribute to Spark MLlib or Related Open-Source Projects
Show steps
Engage with the open-source community by contributing to Spark MLlib or other regression-related projects. This will provide you with practical experience, enhance your technical skills, and connect you with experts in the field.
Show steps
-
Identify open-source projects related to Spark MLlib or regression that align with your interests.
-
Review the project documentation and codebase to understand its goals and functionality.
-
Propose and implement improvements or new features, following the project's contribution guidelines.
Revise Spark Fundamental Concepts
Show steps
Revisit key concepts in Spark, including RDDs, transformations, and actions, to strengthen your understanding before diving into regression models.
Browse courses on
Apache Spark
Show steps
Show steps
Show steps
- Review the Spark documentation or online tutorials on core concepts. (e.g. RDDs, transformations, actions)
- Work through simple Spark examples to refresh your hands-on experience.
Explore Spark Machine Learning Library (MLlib) Tutorials
Show steps
Supplement your course material by following guided tutorials on Spark MLlib, focusing on regression algorithms and techniques to enhance your practical skills.
Browse courses on
Spark MLlib
Show steps
Show steps
Show steps
- Search for tutorials on the official Spark MLlib website or reputable online platforms.
- Select tutorials covering regression topics relevant to the course, such as linear regression or decision trees.
- Follow the tutorials step-by-step, implementing the regression algorithms in PySpark.
Participate in Online Discussion Forums
Show steps
Engage in discussions with fellow learners by participating in online forums or chat groups. Exchanging ideas, asking questions, and providing support can enhance your understanding and foster a sense of community.
Show steps
Show steps
Show steps
- Identify relevant online forums or chat groups related to Spark and regression.
- Actively participate in discussions by sharing your insights, asking questions, and providing feedback to others.
- Be respectful and mindful of diverse perspectives and opinions.
Three other activities
Expand to see all activities and additional details
Show all six activities
Assist Peers in Understanding Regression Concepts
Show steps
Deepen your own understanding by explaining regression concepts to others. This will reinforce your knowledge and allow you to identify areas where you need further improvement.
Show steps
Show steps
Show steps
- Identify opportunities to assist peers who may be struggling with regression concepts.
- Provide clear and concise explanations, using examples and analogies to make concepts relatable.
- Encourage peers to ask questions and engage in discussions.
Develop a Regression Model for a Real-World Dataset
Show steps
Apply your regression skills to a real-world dataset, implementing a regression model in PySpark to solve a specific problem or make predictions. This will demonstrate your ability to apply your knowledge in a practical context.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a dataset that aligns with your interests or a specific industry.
- Load and explore the dataset in PySpark, identifying potential features and target variables.
- Select appropriate regression algorithms and tune them using cross-validation.
- Evaluate the performance of your model and present your findings in a clear and concise manner.
Contribute to Spark MLlib or Related Open-Source Projects
Show steps
Engage with the open-source community by contributing to Spark MLlib or other regression-related projects. This will provide you with practical experience, enhance your technical skills, and connect you with experts in the field.
Show steps
Show steps
Show steps
- Identify open-source projects related to Spark MLlib or regression that align with your interests.
- Review the project documentation and codebase to understand its goals and functionality.
- Propose and implement improvements or new features, following the project's contribution guidelines.
Career center
Learners who complete Machine Learning y Regresión con PySpark. Guía paso a paso will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds, deploys, and maintains machine learning models. This course can aid someone in becoming a Machine Learning Engineer by teaching how to generate Machine Learning models with Spark (MLlib) in Databricks. Machine Learning Engineers need to be capable of building models, and this course can provide instruction on how to do so in a Big Data environment.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. This course can help someone become a Data Scientist by providing instruction on using Spark and MLlib to generate Machine Learning models in a Big Data environment. Machine Learning models are a core tool for data scientists, and someone seeking this career path may find this course particularly helpful.
Data Architect
Data Architect
A Data Architect designs and manages data systems. This course may be useful for someone who hopes to become a Data Architect. Data Architects need to have a strong understanding of how to work with Machine Learning models and Big Data, and this course can help build a foundation for this. In particular, this course covers how to generate Machine Learning models with Spark and MLlib in Databricks.
Decision Scientist
Decision Scientist
A Decision Scientist uses data to help businesses make better decisions. This course may be useful for someone who wishes to become a Decision Scientist. Decision Scientists need to be able to understand and use Machine Learning models, and this course can help build a foundation for this in a Big Data environment.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst analyzes and solves complex business problems. This course may be useful to someone who hopes to become an Operations Research Analyst. Operations Research Analysts may use Machine Learning models, and this course can provide a foundation for this in a Big Data environment. Specifically, this course covers how to generate Machine Learning models with Spark and MLlib in Databricks.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to extract meaningful insights. This course may be useful to someone who wishes to become a Data Analyst. Data Analysts need to be able to generate Machine Learning models, and this course can help build a foundation for doing this in a Big Data environment.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze investments and make recommendations. This course may be useful to someone looking to become a Quantitative Analyst. Quantitative Analysts need to be able to use Machine Learning models, and this course can help provide a foundation for this in a Big Data environment.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to help businesses make better decisions. This course may be useful to someone seeking to become a Financial Analyst. Financial Analysts may need to use Machine Learning models, and this course can help build a foundation for this. In particular, this course covers how to generate Machine Learning models in a Big Data environment, which can be useful for certain financial analysis tasks.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines to ensure the availability, quality, and security of data assets. This course may provide some useful information for someone hoping to become a Data Engineer. Data Engineers need to be able to work with Big Data, and this course can help build a foundation that may be useful for this career.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. This course may be useful to someone who wants to become a Statistician. Statisticians may need to use Machine Learning models, and this course can help build a foundation for this in a Big Data environment.
Actuary
Actuary
An Actuary analyzes and manages financial risk. This course may be useful for someone seeking to become an Actuary. Actuaries may need to use Machine Learning models, and this course can help build a foundation for doing this. In particular, this course covers how to generate Machine Learning models in a Big Data environment, which can be useful for certain actuarial tasks.
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst uses data to help businesses make better decisions. This course may be useful for someone seeking to become a Business Intelligence Analyst. Business Intelligence Analysts need to be able to understand and use Machine Learning models, and this course can help build a foundation for this.
Risk Manager
Risk Manager
A Risk Manager identifies, assesses, and manages risks. This course may be useful to someone who wants to become a Risk Manager. Risk Managers may need to use Machine Learning models, and this course can help build a foundation for this. In particular, this course covers how to generate Machine Learning models in a Big Data environment, which can be useful for certain risk management tasks.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications and systems. This course may be useful for someone who wants to become a Software Engineer, as it covers how to use Spark and MLlib to generate Machine Learning models in a Big Data environment. This is a skill that may be useful for a Software Engineer.
Systems Analyst
Systems Analyst
A Systems Analyst designs, develops, and maintains computer systems. This course may be useful for someone who wants to become a Systems Analyst. Systems Analysts need to have an understanding of how to use Machine Learning models, and this course can help build a foundation. In particular, this course covers how to generate Machine Learning models in a Big Data environment, which is becoming increasingly important for Systems Analysts.
For more career information including salaries, visit:
OpenCourser.com/course/0cu81f/machine
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning y Regresión con PySpark. Guía paso a paso.
Save
Este libro oficial de Apache Spark proporciona una introducción completa al framework, cubriendo la programación básica, el procesamiento de datos y el aprendizaje automático. Es un recurso esencial para quienes buscan construir una base sólida en Spark y sus capacidades de aprendizaje automático.
Este libro fundamental cubre una amplia gama de técnicas de aprendizaje automático y minería de datos. Proporciona información detallada sobre regresión, árboles de decisión, agrupamiento y otras técnicas que son esenciales para comprender y aplicar el modelado de regresión con PySpark.
Este libro clásico proporciona una introducción integral al análisis de regresión y los modelos lineales generalizados. Es un recurso valioso para aquellos que buscan una base sólida en las técnicas estadísticas subyacentes al modelado de regresión.
Save
Comprehensive guide to Apache Spark, covering both the core concepts and advanced topics. It is written by the creators of Spark and provides a deep understanding of the platform.
Esta guía integral cubre todos los aspectos de Python para la ciencia de datos, desde el manejo de datos hasta la visualización y el aprendizaje automático. Proporciona una base sólida en Python y sus bibliotecas esenciales para aquellos que buscan utilizar PySpark para el aprendizaje automático en entornos de Big Data.
Este libro práctico enseña el aprendizaje automático utilizando bibliotecas populares de Python como Scikit-Learn, Keras y TensorFlow. Proporciona una introducción completa a las técnicas de modelado de regresión y su implementación en Python, lo que es beneficioso para aquellos que buscan aplicar PySpark en entornos de Big Data.
Provides a comprehensive overview of machine learning concepts and techniques, with a focus on using Elm for implementation. It covers a wide range of topics, including data preparation, feature engineering, model selection, and evaluation.
Provides a comprehensive overview of machine learning concepts and techniques, with a focus on using JavaScript for implementation. It covers a wide range of topics, including data preparation, feature engineering, model selection, and evaluation.
Provides a comprehensive overview of machine learning concepts and techniques, with a focus on using R for implementation. It covers a wide range of topics, including data preparation, feature engineering, model selection, and evaluation.
Este libro se centra en el uso práctico de Python y PySpark para el aprendizaje automático, brindando ejemplos paso a paso y proyectos del mundo real. Es adecuado para aquellos que desean aplicar técnicas de aprendizaje automático a problemas empresariales utilizando herramientas populares.
Este libro ofrece una introducción completa al aprendizaje automático desde una perspectiva probabilística. Proporciona una base teórica sólida para aquellos que buscan comprender los fundamentos matemáticos del aprendizaje automático y sus aplicaciones en el modelado de regresión con PySpark.
Este libro se enfoca en los métodos estadísticos utilizados en las ciencias atmosféricas. Proporciona una base sólida en el análisis de datos, el modelado estadístico y la inferencia, que son esenciales para aplicar técnicas de aprendizaje automático a problemas de pronóstico meteorológico y climático.
Este libro enseña los fundamentos de la ciencia de datos desde cero, cubriendo temas como la limpieza de datos, el análisis exploratorio y el modelado. Si bien no se centra en PySpark, proporciona una base sólida en los conceptos de ciencia de datos y programación, que es esencial para comprender y aplicar el aprendizaje automático con PySpark.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0cu81f/machine
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Effort
2 horas
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Leire Ahedo
Language
Español
Good to know
Explora los fundamentos de Spark y MLlib, que conforman la base para la ciencia de datos
Proporciona una guía paso a paso sobre la creación de modelos de Machine Learning con PySpark, lo que lo hace accesible para principiantes
Se enfoca en la aplicación práctica, con el objetivo de que los estudiantes desarrollen modelos de regresión avanzados para resolver problemas del mundo real
Está dirigido a aquellos que buscan desarrollar habilidades en el uso de PySpark para el análisis de Big Data y el Machine Learning
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