Acerca de este Curso
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Nivel avanzado

Aprox. 17 horas para completar

Sugerido: This course requires 7.5 to 9 hours of study....

Inglés (English)

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Habilidades que obtendrás

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel avanzado

Aprox. 17 horas para completar

Sugerido: This course requires 7.5 to 9 hours of study....

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
4 horas para completar

Feedback loops and Monitoring

5 videos (Total 19 minutos), 15 lecturas, 4 cuestionarios
5 videos
Feedback Loops and Unit Tests7m
Performance Monitoring and Business Metrics1m
Performance Drift5m
Performance Monitoring Case Study1m
15 lecturas
Feedback loops and unit tests: Through the eyes of our Working Example3m
Feedback loops4m
Unit tests4m
Unit testing in Python3m
Test-Driven Development (TDD)3m
CI/CD3m
Performance Monitoring: Through the eyes of our Working Example3m
Logging3m
Minimal requirements for log files4m
Logging in Python (hands-on)30m
Model performance drift4m
Performance Drift Notebook Review25m
Performance Monitoring Case Study: Through the eyes of our Working Example4m
Getting started (hands-on)2h
Summary/Review6m
4 ejercicios de práctica
Check for Understanding2m
Check for Understanding2m
Check for Understanding2m
End of Module Quiz5m
Semana
2
3 horas para completar

Hands on with Openscale and Kubernetes

3 videos (Total 22 minutos), 6 lecturas, 3 cuestionarios
3 videos
Kubernetes Explained10m
Kubernetes vs. Docker: It's Not an Either/Or Question8m
6 lecturas
Watson OpenScale: Through the eyes of our Working Example4m
Getting started (hands-on)1h
Kubernetes Explained: Through the eyes of our Working Example4m
Introduction to Kubernetes4m
Getting started (hands-on)1h 30m
Summary/Review4m
3 ejercicios de práctica
Check for Understanding2m
Check for Understanding2m
End of Module Quiz5m
Semana
3
3 horas para completar

Capstone: Pulling it all together (Part 1)

10 lecturas, 1 cuestionario
10 lecturas
Capstone: Through the eyes of our Working Example4m
What is in the Capstone and associated Review?4m
Review of Course 1: Business Priorities and Data Ingestion4m
Review of Course 2: Data Analysis and Hypothesis Testing5m
Review of Course 3: Feature Engineering and Bias Detection5m
Review of Course 4: Machine Learning, Visual Recognition, and NLP12m
Review of Course 5: Enterprise Model Deployment4m
About the data3m
Capstone Assignment 1: Through the eyes of our Working Example4m
Capstone Part 1: Getting Started (hands-on)2h
1 ejercicio de práctica
Capstone - Part 1 Quiz6m
Semana
4
5 horas para completar

Capstone: Pulling it all together (Part 2)

4 lecturas, 3 cuestionarios
4 lecturas
Capstone Assignment 2: Through the eyes of our Working Example4m
Capstone Part 2: Getting started (hands-on)2h
Capstone Part 3: Getting started (hands-on)2h
Solution Files1m
2 ejercicios de práctica
Capstone - Part 2 Quiz6m
Capstone - Part 3 Quiz6m

Instructores

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Mark J Grover

Digital Content Delivery Lead
IBM Data & AI Learning
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Ray Lopez, Ph.D.

Data Science Curriculum Leader
IBM Data & Artificial Intelligence

Acerca de IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

Acerca de Programa especializado IBM AI Enterprise Workflow

This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow....
IBM AI Enterprise Workflow

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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