Acerca de este Curso
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Nivel avanzado

Aprox. 5 horas para completar

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

Inglés (English)

Subtítulos: Inglés (English)

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. 5 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

Data transforms and feature engineering

6 videos (Total 31 minutos), 14 lecturas, 5 cuestionarios
6 videos
Introduction to Class Imbalance1m
Class Imbalance Deep Dive9m
Introduction to Dimensionality Reduction2m
Dimension Reduction13m
Case study intro / Feature Engineering1m
14 lecturas
Data Transformation: Through the eyes of our Working Example3m
Transforms / Scikit-learn3m
Pipelines3m
Class imbalance: Through the eyes of our Working Example3m
Class Imbalance5m
Sampling techniques2m
Models that naturally handle imbalance2m
Data bias2m
Dimensionality Reduction: Through the eyes of our Working Example3m
Why is dimensionality reduction important?3m
Dimensionality reduction and Topic models5m
Topic modeling: Through the eyes of our Working Example3m
Getting Started with the topic modeling case study (hands-on)2h
Data transforms and feature engineering: Summary/Review5m
5 ejercicios de práctica
Getting Started: Check for Understanding2m
Class imbalance, data bias: Check for Understanding2m
Dimensionality Reduction: Check for Understanding3m
CASE STUDY - Topic modeling: Check for Understanding2m
Data transforms and feature engineering:End of Module Quiz10m
Semana
2
3 horas para completar

Pattern recognition and data mining best practices

4 videos (Total 10 minutos), 11 lecturas, 5 cuestionarios
4 videos
Introduction to Outliers2m
Outlier Detection3m
Introduction to Unsupervised learning2m
11 lecturas
ai360: Through the eyes of our Working Example3m
Introduction to ai360 (hands-on)15m
Outlier detection: Through the eyes of our Working Example3m
Outliers3m
Unsupervised learning: Through the eyes of our Working Example3m
An overview of unsupervised learning2m
Clustering3m
Clustering evaluation3m
Clustering: Through the eyes of our Working Example3m
Getting Started with the clustering case study (hands-on)2h 10m
Pattern recognition and data mining best practices: Summary/Review4m
5 ejercicios de práctica
ai360 Tutorial: Check for Understanding2m
Outlier detection: Check for Understanding2m
Unsupervised learning: Check for Understanding2m
CASE STUDY - Clustering: Check for Understanding2m
Pattern recognition and data mining best practices: End of Module Quiz12m

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.

  • This course assumes that you are already familiar with basic data science concepts including probability and statistics, linear algebra, machine learning, and the use of Python and Jupyter. It is assumed you have completed the first two courses of the specialization: AI Workflow: Business Priorities and Data Ingestion, AI Workflow: Data Analysis and Hypothesis Testing.

  • No. Most of the exercises may be completed with open source tools running on your personal computer. However, the exercises are designed with an enterprise focus and are intended to be run in an enterprise environment that allows for easier sharing and collaboration. The exercises in the last two modules of the course are heavily focused on deployment and testing of machine learning models and use the IBM Watson tooling found on the IBM Cloud.

  • Yes. All IBM Cloud Data and AI services are based upon open source technologies.

  • The exercises in the course may be completed by anyone using the IBM Cloud "Lite" plan, which is free for use.

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