Course
Designing Machine Learning Workflows in Python
AdvancedSkill Level
Updated 11/2024PythonMachine Learning4 hr16 videos51 Exercises4,200 XP12,423Statement of Accomplishment
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Prerequisites
Python ToolboxUnsupervised Learning in PythonSupervised Learning with scikit-learn1
The Standard Workflow
In this chapter, you will be reminded of the basics of a supervised learning workflow, complete with model fitting, tuning and selection, feature engineering and selection, and data splitting techniques. You will understand how these steps in a workflow depend on each other, and recognize how they can all contribute to, or fight against overfitting: the data scientist's worst enemy. By the end of the chapter, you will already be fluent in supervised learning, and ready to take the dive towards more advanced material in later chapters.
2
The Human in the Loop
In the previous chapter, you perfected your knowledge of the standard supervised learning workflows. In this chapter, you will critically examine the ways in which expert knowledge is incorporated in supervised learning. This is done through the identification of the appropriate unit of analysis which might require feature engineering across multiple data sources, through the sometimes imperfect process of labeling examples, and through the specification of a loss function that captures the true business value of errors made by your machine learning model.
3
Model Lifecycle Management
In the previous chapter, you employed different ways of incorporating feedback from experts in your workflow, and evaluating it in ways that are aligned with business value. Now it is time for you to practice the skills needed to productize your model and ensure it continues to perform well thereafter by iteratively improving it. You will also learn to diagnose dataset shift and mitigate the effect that a changing environment can have on your model's accuracy.
4
Unsupervised Workflows
In the previous chapters you established a solid foundation in supervised learning, complete with knowledge of deploying models in production but always assumed you a labeled dataset would be available for your analysis. In this chapter, you take on the challenge of modeling data without any, or with very few, labels. This takes you into a journey into anomaly detection, a kind of unsupervised modeling, as well as distance-based learning, where beliefs about what constitutes similarity between two examples can be used in place of labels to help you achieve levels of accuracy comparable to a supervised workflow. Upon completing this chapter, you will clearly stand out from the crowd of data scientists in confidently knowing what tools to use to modify your workflow in order to overcome common real-world challenges.
Designing Machine Learning Workflows in Python
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FAQs
What real-world domains are covered in this advanced ML course?
You will work with datasets from personalized healthcare and cybersecurity, applying cutting-edge scikit-learn techniques to build production-ready machine learning pipelines.
What four key skills does this course teach for production ML?
You will learn exhaustive model tuning, leveraging domain expertise, monitoring model performance in production, and handling poorly or scarcely labeled data.
Does this course address what happens when models perform worse in production?
Yes. The course directly tackles why models degrade in production and teaches you how to monitor performance and deal with deterioration to build pipelines that last.
How many prerequisites does this advanced course require?
Eight prerequisites are required, including pandas, scikit-learn, unsupervised learning, Python functions, and statistics. This course is designed for experienced Python data scientists.
Does the course cover working with limited labeled data?
Yes. One of the four core skills is learning how to deal with poorly or scarcely labeled data, a common real-world challenge that many ML courses do not address.
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