This course synthesizes everything your have learned in the applied machine learning specialization. You will now walk through a complete machine learning project to prepare a machine learning maintenance roadmap. You will understand and analyze how to deal with changing data. You will also be able to identify and interpret potential unintended effects in your project. You will understand and define procedures to operationalize and maintain your applied machine learning model. By the end of this course you will have all the tools and understanding you need to confidently roll out a machine learning project and prepare to optimize it in your business context.
Este curso faz parte do Programa de cursos integrados Machine Learning: Algorithms in the Real World
oferecido por
Programa de cursos integrados Machine Learning: Algorithms in the Real WorldAlberta Machine Intelligence InstituteInformações sobre o curso
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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Aprox. 13 horas para completar
Inglês
Legendas: Inglês
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Aprox. 13 horas para completar
Inglês
Legendas: Inglês
oferecido por
Alberta Machine Intelligence Institute
The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based
Programa - O que você aprenderá com este curso
4 horas para concluir
Machine Learning Strategy
This week we'll present tools for understanding the overall strategy your business needs in order to see the best returns on ML investment. From understanding the current status to navigating ownership and setting up a team, this week is about understanding applied machine learning in a successful business context.
4 horas para concluir
8 vídeos (Total 42 mín.), 1 leitura, 7 testes
8 videos
ML Readiness6min
Risk Mitigation5min
Experimental Mindset5min
Build/Buy/Partner7min
Setting up a Team5min
Understanding and Communicating Change7min
Weekly Summary2min
1 leituras
IP questions10min
6 exercícios práticos
ML Readiness Review10min
Risk Mitigation Review10min
Experimental Mindset Review10min
Build/Buy/Partner Review30min
Setting up a Team Review5min
Communicating Change Review5min
2 horas para concluir
Responsible Machine Learning
This week we'll talk about the broader context of machine learning: how as developers we have responsibilities regarding how our technology will be used. Using case studies and existing frameworks we'll give you the tools to figure out your own ethical approach to realize the best outcomes while deploying machine learning in the real world.
2 horas para concluir
6 vídeos (Total 27 mín.)
6 videos
Positive Feedback Loops & Negative Feedback Loops6min
Metric Design & Observing Behaviours6min
Secondary Effects of Optimization4min
Regulatory Concerns3min
Weekly Summary2min
6 exercícios práticos
AI4Good Review5min
Feedback Loops Review5min
Metric Design Review5min
Secondary effects Review5min
Regulatory Concerns Review5min
Responsible Machine Learning Review30min
2 horas para concluir
Machine Learning in Production & Planning
An important aspect of machine learning in the real world is considering how your machine learning models are integrated with existing systems, and what effect they have on your operations. This week we'll review things you should consider as you turn QuAMs and machine learning models into operational tools.
2 horas para concluir
8 vídeos (Total 33 mín.)
8 videos
Users Break Things3min
Time & Space complexity in production5min
When do I retrain the model?4min
Logging ML Model Versioning4min
Knowledge Transfer4min
Reporting Performance to Stakeholders4min
Weekly Summary2min
7 exercícios práticos
Integrating Info Systems Review5min
Complexity in Production Review5min
Retrain the Model Review5min
ML Versioning Review5min
Knowledge Transfer Review5min
Reporting to Stakeholders Review5min
Machine Learning in Production and Planning Review30min
5 horas para concluir
Care and Feeding of your Machine Learning System
Work doesn't end just because your model is deployed! In our final week we'll go over all the things you need to consider in the context of an actual working system.
5 horas para concluir
9 vídeos (Total 45 mín.)
9 videos
MLPL Recap9min
Post Deployment Challenges6min
QuAM Monitoring and Logging5min
QuAM Testing5min
QuAM Maintenance3min
QuAM Updating5min
Separating Datastack from Production3min
Dashboard Essentials & Metrics Monitoring5min
Weekly Summary1min
7 exercícios práticos
Post Deployment Challenges Review5min
Monitoring & Logging Review5min
Testing Review5min
Maintenance Review5min
Updating Review5min
Separating Datastack from Production Review5min
Dashboard Monitoring Review5min
Avaliações
Principais avaliações do OPTIMIZING MACHINE LEARNING PERFORMANCE
por EG8 de Jan de 2020
The whole specialization is extremely useful for people starting in ML. Highly recommended!
Sobre Programa de cursos integrados Machine Learning: Algorithms in the Real World
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application.
After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
O que recebo ao me inscrever nesta Especialização?
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.
Qual é a política de reembolso?
Se você se inscrever, terá 7 dias para testar sem custo e, durante este período, pode cancelar sem multa. Depois disso, não reembolsamos, mas você pode cancelar sua inscrição a qualquer momento. Veja nossa política para o reembolso total.
Existe algum auxílio financeiro disponível?
Sim, a Coursera oferece auxílio financeiro ao aluno que não possa pagar a taxa. Faça a solicitação clicando no link Auxílio Financeiro, abaixo do botão "Inscreva-se" à esquerda. Preencha uma solicitação e será notificado caso seja aprovado. Você terá que completar esta etapa para cada curso na Especialização, incluindo o Trabalho de Conclusão de Curso. Saiba mais .
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