Predictive Modeling with Python

Overview

Dive into Predictive Modeling with Python, focusing on regression using the California Housing Dataset. Through hands-on coding, this path teaches you how to build and refine models. Master regression techniques and predictive modeling to make informed predictions.

Syllabus

Course 1: Introduction to Predictive Modeling
Course 2: Data Preprocessing for Predictive Modeling
Course 3: Regression Models for Prediction
Course 4: Advanced Machine Learning Models for Prediction
Course 5: Model Evaluation and Optimization

Courses

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3 hours
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Initiate your understanding of predictive modeling by exploring the fundamental workings and purposes of these models. Gain insights into how predictive models can guide decision-making across industries and sectors.
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3 hours
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Unveil how preprocessing refines data to make predictive models more effective. Learn to handle missing values, outliers and categorical variables, ensuring data consistency and integrity.
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2 hours
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Grasp the basics of using different regression models for predictive modeling. Learn how to establish polynomial, lasso and ridge regression models within Python.
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3 hours
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As you become more proficient with regression models, this course will introduce you to more advanced models available in the Scikit-Learn library. Explore popular machine learning algorithms, including Support Vector Machines, decision trees, random forest and neural networks.
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2 hours
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Any predictive regression model is only as good as its performance, this course delves into advanced techniques for evaluating and optimizing regression models. Explore sophisticated strategies to enhance predictive accuracy and model robustness.