Intro to Predictive Analytics Using Python

Overview

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"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.

Syllabus

Module 1: Introduction to Predictive Analytics and Regressions

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.

Module 2: Decision Trees and Introduction to Advanced Predictive Analytics and Random Forests

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.

Module 3: Introduction to Unsupervised Learning and Clustering

Module 3 explores unsupervised learning and clustering, guiding you through the nuances of model comparison and the art of identifying patterns without predefined labels.