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Build a Regression Model using PyCaret

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In this 1-hour long project-based course, you will create an end-to-end Regression model using PyCaret a low-code Python open-source Machine Learning library. The goal is to build a model that can accurately predict the strength of concrete based on several fatures. You will learn how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for regression. Here are the main steps you will go through: frame the problem, get and prepare the data, discover and visualize the data, create the transformation pipeline, build, evaluate, interpret and deploy the model. This guided project is for seasoned Data Scientists who want to build a accelerate the efficiency in building POC and experiments by using a low-code library. It is also for Citizen data Scientists (professionals working with data) by using the low-code library PyCaret to add machine learning models to the analytics toolkit In order to be successful in this project, you should be familiar with Python and the basic concepts on Machine Learning Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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Dive into "Build a Regression Model using PyCaret," a 1-hour project-based course tailored for seasoned and aspiring Data Scientists. Under the expert guidance of Coursera, you'll master PyCaret, a low-code Python Machine Learning library, to automate and streamline regression model creation. This hands-on course covers problem framing, data preparation, visualization, model building, evaluation, and deployment. Ideal for professionals familiar with Python and basic Machine Learning, it enhances your analytics toolkit with efficient POC and experimental builds. Perfect for those in North America, with broader accessibility coming soon. Enjoy flexible learning with a Starter subscription.