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Leire Ahedo
Es un curso práctico y efectivo para aprender a generar modelos de Machine Learning con PySpark en un entorno de Big Data para predecir el "Churn" del cliente. Te enseñaremos desde cero los fundamentos de Spark y MLlib, y acabarás desarrollando avanzados modelos de Machine Learning con MLlib y PySpark.
Register for this course and see more details by visiting:
OpenCourser.com/course/gzrktm/ml
What's inside
Syllabus
Machine Learning y Big Data con PySpark para la retención de clientes
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
Desarrolla modelos de ML avanzados, que son habilidades centrales para la retención de clientes en un entorno de Big Data
Enseña Spark y MLlib, que son bibliotecas de código abierto ampliamente utilizadas en la industria
Proporciona una comprensión práctica de las técnicas de ML, lo que permite a los alumnos aplicarlas a casos de negocio del mundo real
Ofrece un enfoque práctico, que es muy valorado por los empleadores y gerentes de contratación
Requiere un conocimiento previo de PySpark, lo que puede ser un obstáculo para los principiantes
El curso es impartido por Leire Ahedo, una instructora experimentada en el campo de la ciencia de datos
Save this course
Save ML y Big Data con PySpark para la retención de clientes to your list so you can find it easily later:
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Reviews summary
Effective big data course
Students who have reviewed this course called it 'effective' and 'practical' as well as 'well-explained' with 'good materials.' Many reviewers liked the 'clear' pipeline and would highly recommend the instructor.
Good materials
"gran material"
Great instructor
"buena instructora"
Well-explained
"muy bien explicado"
Clear pipeline
"Muy ordenado y claro el pipeline"
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 ML y Big Data con PySpark para la retención de clientes with these
activities:
Practica la programación en Python
Show steps
Actualiza tus habilidades de programación en Python para prepararte para el uso de PySpark y MLlib en el curso.
Browse courses on
Python
Show steps
-
Resuelve problemas de programación en línea
-
Trabaja en proyectos personales
-
Participa en desafíos de programación
Documentación de PySpark y MLlib
Show steps
Amplía tus conocimientos compilando recursos esenciales, como documentación y tutoriales, relacionados con PySpark y MLlib.
Browse courses on
Pyspark
Show steps
-
Busca y reúne documentación oficial de PySpark y MLlib
-
Encuentra tutoriales y guías paso a paso
-
Crea un repositorio centralizado para almacenar y organizar los recursos
Ejercicios de PySpark y MLlib
Show steps
Mejora tu comprensión de PySpark y MLlib resolviendo ejercicios prácticos para reforzar lo aprendido en clase.
Browse courses on
Pyspark
Show steps
-
Resuelve problemas de manipulación de datos con PySpark
-
Aplica transformaciones y acciones de MLlib a conjuntos de datos
-
Crea modelos de aprendizaje automático con MLlib y PySpark
-
Evalúa y compara el rendimiento de los modelos
Three other activities
Expand to see all activities and additional details
Show all six activities
Sesiones de práctica grupal
Show steps
Mejora tus habilidades de resolución de problemas y pensamiento crítico participando en sesiones de práctica con tus compañeros.
Show steps
-
Forma un grupo de estudio con otros estudiantes
-
Revisa los conceptos de clase y resuelve problemas juntos
-
Comparte ideas y perspectivas diferentes
Tutoriales guiados de MLlib y PySpark
Show steps
Refuerza tu comprensión de PySpark y MLlib siguiendo tutoriales guiados para practicar técnicas y habilidades específicas.
Browse courses on
Pyspark
Show steps
-
Busca tutoriales sobre temas específicos que desees mejorar
-
Sigue los tutoriales paso a paso y completa los ejercicios
-
Experimenta con diferentes parámetros y opciones
Crea un modelo de Machine Learning en PySpark
Show steps
Fortalece tus habilidades prácticas creando un modelo de Machine Learning completo desde cero utilizando PySpark.
Browse courses on
Pyspark
Show steps
-
Recopila y prepara un conjunto de datos
-
Aplica algoritmos de MLlib para entrenar un modelo
-
Evalúa el modelo y optimiza sus parámetros
-
Implementa y despliega el modelo en un entorno real
Practica la programación en Python
Show steps
Actualiza tus habilidades de programación en Python para prepararte para el uso de PySpark y MLlib en el curso.
Browse courses on
Python
Show steps
Show steps
Show steps
- Resuelve problemas de programación en línea
- Trabaja en proyectos personales
- Participa en desafíos de programación
Documentación de PySpark y MLlib
Show steps
Amplía tus conocimientos compilando recursos esenciales, como documentación y tutoriales, relacionados con PySpark y MLlib.
Browse courses on
Pyspark
Show steps
Show steps
Show steps
- Busca y reúne documentación oficial de PySpark y MLlib
- Encuentra tutoriales y guías paso a paso
- Crea un repositorio centralizado para almacenar y organizar los recursos
Ejercicios de PySpark y MLlib
Show steps
Mejora tu comprensión de PySpark y MLlib resolviendo ejercicios prácticos para reforzar lo aprendido en clase.
Browse courses on
Pyspark
Show steps
Show steps
Show steps
- Resuelve problemas de manipulación de datos con PySpark
- Aplica transformaciones y acciones de MLlib a conjuntos de datos
- Crea modelos de aprendizaje automático con MLlib y PySpark
- Evalúa y compara el rendimiento de los modelos
Three other activities
Expand to see all activities and additional details
Show all six activities
Sesiones de práctica grupal
Show steps
Mejora tus habilidades de resolución de problemas y pensamiento crítico participando en sesiones de práctica con tus compañeros.
Show steps
Show steps
Show steps
- Forma un grupo de estudio con otros estudiantes
- Revisa los conceptos de clase y resuelve problemas juntos
- Comparte ideas y perspectivas diferentes
Tutoriales guiados de MLlib y PySpark
Show steps
Refuerza tu comprensión de PySpark y MLlib siguiendo tutoriales guiados para practicar técnicas y habilidades específicas.
Browse courses on
Pyspark
Show steps
Show steps
Show steps
- Busca tutoriales sobre temas específicos que desees mejorar
- Sigue los tutoriales paso a paso y completa los ejercicios
- Experimenta con diferentes parámetros y opciones
Crea un modelo de Machine Learning en PySpark
Show steps
Fortalece tus habilidades prácticas creando un modelo de Machine Learning completo desde cero utilizando PySpark.
Browse courses on
Pyspark
Show steps
Show steps
Show steps
- Recopila y prepara un conjunto de datos
- Aplica algoritmos de MLlib para entrenar un modelo
- Evalúa el modelo y optimiza sus parámetros
- Implementa y despliega el modelo en un entorno real
Career center
Learners who complete ML y Big Data con PySpark para la retención de clientes will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist analyzes data, designs ML models, and deploys these models to production. Coursework in ML and Big Data will equip you to aggregate data from a variety of sources, wrangle that data, and conduct statistical analysis to derive insights that can be used for business decisions. Further, you will learn to use PySpark to build and deploy these analytical models. PySpark is particularly useful when dealing with large datasets. Whether working in academia, industry, or finance, there is high demand for professionals who have strong data science skills and can implement ML solutions at scale.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds, deploys, monitors, and maintains ML models. This course will equip you with the skills necessary to build ML models at scale using MLlib with PySpark on Databricks. You will also learn the basics of Spark, an open-source computational framework, and MLlib, a scalable machine learning library that runs on Spark. These technologies help automate data ingestion, cleansing, preparation, and transformation, which are common tasks for a Machine Learning Engineer.
Data Analyst
Data Analyst
A Data Analyst works with data to provide insights and solve business problems. This course may be helpful for those who aspire to be a Data Analyst because it provides foundational knowledge in ML and Big Data. Through this coursework you will learn about data pipelines, data manipulation, and statistical analysis. These foundational skills can help Data Analysts solve complex problems and make data-driven recommendations.
Software Engineer
Software Engineer
A Software Engineer designs, develops, tests, and deploys software systems. This course may be helpful for those who aspire to be a Software Engineer because it provides foundational knowledge in ML and Big Data. Through this coursework you will learn about data pipelines, data manipulation, and statistical analysis. Further, you will use these skills to build ML models in PySpark to address business problems. These skills would benefit the work of a Software Engineer.
Business Analyst
Business Analyst
A Business Analyst works with stakeholders to understand business needs and translate those needs into actionable insights. This course may be helpful for those who aspire to be a Business Analyst because it provides foundational knowledge in ML and Big Data. Through this coursework you will learn about data pipelines, data manipulation, and statistical analysis. These foundational knowledge and skills would be helpful for a Business Analyst as they work with data scientists and engineers to understand and communicate the business value of ML solutions.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data. This course may be helpful for those who aspire to be a Quantitative Analyst because it provides foundational knowledge in ML and Big Data. Through this coursework you will learn about data pipelines, data manipulation, and statistical analysis. These foundational knowledge and skills would be helpful for a Quantitative Analyst as they work with data scientists and engineers to understand and implement ML solutions for financial applications.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines. Coursework in this course will help build a foundation in ML and Big Data. Through this coursework you will learn about data pipelines, data manipulation, and statistical analysis. These foundational knowledge and skills would be helpful for a Data Engineer who needs to understand the ML process to build scalable and reliable data solutions.
Data Architect
Data Architect
A Data Architect designs and builds data architectures. This course may be helpful for those who aspire to be a Data Architect because it provides foundational knowledge in ML and Big Data. Through this coursework you will learn about data pipelines, data manipulation, and statistical analysis. These foundational knowledge and skills would be helpful for a Data Architect as they work with data scientists and engineers to understand and implement ML solutions within the context of the overall data architecture.
Product Manager
Product Manager
A Product Manager works with stakeholders to define, develop, and launch products. This course may be helpful for those who aspire to be a Product Manager because it provides foundational knowledge in ML and Big Data. Through this coursework you will learn about data pipelines, data manipulation, and statistical analysis. These foundational knowledge and skills would be helpful for a Product Manager as they work with data scientists and engineers to understand and communicate the business value of ML solutions for product development.
Big Data Engineer
Big Data Engineer
A Big Data Engineer designs and builds systems to process and manage large datasets. This course may be helpful for those who aspire to be a Big Data Engineer because it provides foundational knowledge in ML and Big Data. Through this coursework you will learn about data pipelines, data manipulation, and statistical analysis. These foundational knowledge and skills would be helpful for a Big Data Engineer as they work with data scientists and engineers to understand and implement ML solutions at scale.
Data Science Manager
Data Science Manager
A Data Science Manager leads and manages a team of data scientists. This course may be helpful for those who aspire to be a Data Science Manager because it provides foundational knowledge in ML and Big Data. Through this coursework you will learn about data pipelines, data manipulation, and statistical analysis. These foundational knowledge and skills would be helpful for a Data Science Manager as they work with data scientists and engineers to understand and implement ML solutions and provide guidance and mentorship to the team.
Research Scientist
Research Scientist
A Research Scientist conducts research in a specialized field. This course may be helpful for those who aspire to be a Research Scientist because it provides foundational knowledge in ML and Big Data. Through this coursework you will learn about data pipelines, data manipulation, and statistical analysis. These foundational knowledge and skills would be helpful for a Research Scientist as they work to develop new ML algorithms and techniques.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. This course may be helpful for those who aspire to be a Statistician because it provides foundational knowledge in ML and Big Data. Through this coursework you will learn about data pipelines, data manipulation, and statistical analysis. These foundational knowledge and skills would be helpful for a Statistician as they work to develop and implement statistical models and techniques.
IT Consultant
IT Consultant
An IT Consultant provides consulting services to help organizations with their IT needs. This course may be helpful for those who aspire to be an IT Consultant because it provides foundational knowledge in ML and Big Data. Through this coursework you will learn about data pipelines, data manipulation, and statistical analysis. These foundational knowledge and skills would be helpful for an IT Consultant as they work with clients to understand and implement ML solutions.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. This course may be helpful for those who aspire to be a Database Administrator because it provides foundational knowledge in ML and Big Data. Through this coursework you will learn about data pipelines, data manipulation, and statistical analysis. These foundational knowledge and skills would be helpful for a Database Administrator as they work to implement ML solutions within the context of the overall data architecture.
For more career information including salaries, visit:
OpenCourser.com/course/gzrktm/ml
Reading list
We've selected eight 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
ML y Big Data con PySpark para la retención de clientes .
Save
Comprehensive guide to Apache Spark, covering both the fundamentals and advanced topics such as streaming data, graph processing, and machine learning.
Classic textbook on statistical learning, covering topics such as linear models, regression, classification, and clustering.
Offers a comprehensive guide to machine learning, covering both supervised and unsupervised learning methods. It provides hands-on examples and code snippets in Python to help readers apply the concepts in practice.
Practical guide to deep learning, focusing on building and training neural networks using Python. It covers topics such as convolutional neural networks, recurrent neural networks, and deep reinforcement learning.
Practical guide to data analysis using Python. It covers the basics of Python programming, as well as advanced topics such as data manipulation, visualization, and modeling.
Concise and accessible introduction to machine learning, covering the basics of supervised and unsupervised learning in just 100 pages.
Provides a comprehensive introduction to data science, covering topics such as data cleaning, exploration, visualization, and modeling. It uses Python and Jupyter notebooks to demonstrate practical applications.
Provides a gentle introduction to machine learning concepts and algorithms. It uses Python and Jupyter notebooks to demonstrate practical applications, making it accessible to beginners.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gzrktm/ml
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Effort
2 horas
Level
Advanced
Via
Coursera
Institution
Coursera Project Network
Instructor
Leire Ahedo
Language
Español
Good to know
Desarrolla modelos de ML avanzados, que son habilidades centrales para la retención de clientes en un entorno de Big Data
Enseña Spark y MLlib, que son bibliotecas de código abierto ampliamente utilizadas en la industria
Proporciona una comprensión práctica de las técnicas de ML, lo que permite a los alumnos aplicarlas a casos de negocio del mundo real
Ofrece un enfoque práctico, que es muy valorado por los empleadores y gerentes de contratación
Requiere un conocimiento previo de PySpark, lo que puede ser un obstáculo para los principiantes
El curso es impartido por Leire Ahedo, una instructora experimentada en el campo de la ciencia de datos
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