"Introduction to Predictive Analytics and Advanced Predictive Analytics Using Python" is specially designed to enhance your skills in building, refining, and implementing predictive models using Python. This course serves as a comprehensive introduction to predictive analytics, beginning with the fundamentals of linear and logistic regression. These models are the cornerstone of predictive analytics, enabling you to forecast future events by learning from historical data. We cover a bit of the theory behind these models, but in particular, their application in real-world scenarios and the process of evaluating their performance to ensure accuracy and reliability. As the course progresses, we delve deeper into the realm of machine learning with a focus on decision trees and random forests. These techniques represent a more advanced aspect of supervised learning, offering powerful tools for both classification and regression tasks. Through practical examples and hands-on exercises, you'll learn how to build these models, understand their intricacies, and apply them to complex datasets to identify patterns and make predictions. Additionally, we introduce the concepts of unsupervised learning and clustering, broadening your analytics toolkit, and providing you with the skills to tackle data without predefined labels or categories. By the end of this course, you'll not only have a thorough understanding of various predictive analytics techniques, but also be capable of applying these techniques to solve real-world problems, setting the stage for continued growth and exploration in the field of data analytics.
Intro to Predictive Analytics Using Python
Ce cours fait partie de Spécialisation How to Use Data
Instructeur : Brandon Krakowsky
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Ce que vous apprendrez
Implement data preprocessing and model training procedures for regression.
Interpret feature importance in decision trees and random forests.
Explain the difference between supervised and unsupervised learning.
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Il y a 3 modules dans ce cours
Module 1 introduces you to predictive analytics, covering essential models such as linear and logistic regression. This is where you start to learn how to forecast future trends from historical data.
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20 vidéos4 lectures2 devoirs2 éléments d'application
Module 2 expands your knowledge into decision trees and random forests, offering a deeper dive into more complex supervised learning models that enhance your predictive analytics capabilities.
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16 vidéos4 lectures2 devoirs2 éléments d'application
Module 3 explores unsupervised learning and clustering, guiding you through the nuances of model comparison and the art of identifying patterns without predefined labels.
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8 vidéos4 lectures3 devoirs1 élément d'application
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University of Pennsylvania
University of Pennsylvania
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