Machine learning is not just a single task or even a small group of tasks; it is an entire process, one that practitioners must follow from beginning to end. It is this process—also called a workflow—that enables the organization to get the most useful results out of their machine learning technologies. No matter what form the final product or service takes, leveraging the workflow is key to the success of the business's AI solution.
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Follow a Machine Learning Workflow
Ce cours fait partie de CertNexus Certified Artificial Intelligence Practitioner Certificat Professionnel
Instructeur : Renée Cummings
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Ce que vous apprendrez
Collect and prepare a dataset to use for training and testing a machine learning model.
Analyze a dataset to gain insights.
Set up and train a machine learning model as needed to meet business requirements.
Communicate the findings of a machine learning project back to the organization.
Compétences que vous acquerrez
- Catégorie : Modeling
- Catégorie : Process Management
- Catégorie : Artificial Intelligence (AI)
- Catégorie : Data Analysis
- Catégorie : Machine Learning
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Il y a 6 modules dans ce cours
The previous course in this specialization provided an overview of the machine learning workflow. Now, in this course, you'll dive deeper and actually go through the process step by step. In this first module, you'll begin by collecting the data that will be used as input to your machine learning projects.
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9 vidéos4 lectures2 devoirs1 sujet de discussion2 laboratoires non notés
You've formulated a machine learning problem, and have identified a potential dataset to use. Now you'll analyze the dataset to develop ideas on how to make the best use of the information it contains as you prepare to create your initial machine learning model.
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15 vidéos5 lectures1 devoir1 sujet de discussion3 laboratoires non notés
Before a dataset can be used with a machine learning model, there are typically various tasks you need to perform to ensure that data is an optimal state. In this module, you'll use various methods to prepare the data.
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9 vidéos4 lectures2 devoirs1 sujet de discussion1 laboratoire non noté
To set up a machine learning model in an environment like Python, you must determine the algorithm that will produce the results you're after, and then use it to create a model based on your training data. After the initial setup, it may take multiple tests and refinements to produce a model that meets your requirements.
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13 vidéos3 lectures1 devoir1 sujet de discussion4 laboratoires non notés
Now that you've finished training and tuning a machine learning model, you can turn your attention to deploying it. This may amount to producing a report based on your findings, or it may be much more involved, particularly if it will be incorporated into repeatable processes or become part of a software solution. In either case, finalization is the crucial conclusion to the machine learning workflow.
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8 vidéos3 lectures1 devoir2 évaluations par les pairs1 sujet de discussion
You'll work on a project in which you'll apply your knowledge of the material in this course to a practical scenario.
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1 évaluation par les pairs1 laboratoire non noté
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Révisé le 31 août 2023
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