Informações sobre o curso
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Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível avançado

Aprox. 6 horas para completar

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

Inglês

Legendas: Inglês

Habilidades que você terá

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível avançado

Aprox. 6 horas para completar

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

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
4 horas para concluir

Data transforms and feature engineering

6 vídeos (Total 31 mín.), 14 leituras, 5 testes
6 videos
Introduction to Class Imbalance1min
Class Imbalance Deep Dive9min
Introduction to Dimensionality Reduction2min
Dimension Reduction13min
Case study intro / Feature Engineering1min
14 leituras
Data Transformation: Through the eyes of our Working Example3min
Transforms / Scikit-learn3min
Pipelines3min
Class imbalance: Through the eyes of our Working Example3min
Class Imbalance5min
Sampling techniques2min
Models that naturally handle imbalance2min
Data bias2min
Dimensionality Reduction: Through the eyes of our Working Example3min
Why is dimensionality reduction important?3min
Dimensionality reduction and Topic models5min
Topic modeling: Through the eyes of our Working Example3min
Getting Started with the topic modeling case study (hands-on)2h
Data transforms and feature engineering: Summary/Review5min
5 exercícios práticos
Getting Started: Check for Understanding2min
Class imbalance, data bias: Check for Understanding2min
Dimensionality Reduction: Check for Understanding3min
CASE STUDY - Topic modeling: Check for Understanding2min
Data transforms and feature engineering:End of Module Quiz10min
Semana
2
3 horas para concluir

Pattern recognition and data mining best practices

4 vídeos (Total 10 mín.), 11 leituras, 5 testes
4 videos
Introduction to Outliers2min
Outlier Detection3min
Introduction to Unsupervised learning2min
11 leituras
ai360: Through the eyes of our Working Example3min
Introduction to ai360 (hands-on)15min
Outlier detection: Through the eyes of our Working Example3min
Outliers3min
Unsupervised learning: Through the eyes of our Working Example3min
An overview of unsupervised learning2min
Clustering3min
Clustering evaluation3min
Clustering: Through the eyes of our Working Example3min
Getting Started with the clustering case study (hands-on)2h 10min
Pattern recognition and data mining best practices: Summary/Review4min
5 exercícios práticos
ai360 Tutorial: Check for Understanding2min
Outlier detection: Check for Understanding2min
Unsupervised learning: Check for Understanding2min
CASE STUDY - Clustering: Check for Understanding2min
Pattern recognition and data mining best practices: End of Module Quiz12min

Instrutores

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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

Sobre 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....

Sobre Programa de cursos integrados 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

Perguntas Frequentes – FAQ

  • Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

  • Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.

  • 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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