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Meaningful Predictive Modeling

This course is part of Python Data Products for Predictive Analytics Specialization

Instructors: Julian McAuley +1 more

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What you'll learn

Understand the definitions of simple error measures (e.g. MSE, accuracy, precision/recall).

Evaluate the performance of regressors / classifiers using the above measures.

Understand the difference between training/testing performance, and generalizability.

Understand techniques to avoid overfitting and achieve good generalization performance.

Skills you'll gain

Verification And Validation

Supervised Learning

Predictive Modeling

Regression Analysis

Data Validation

Statistical Methods

Natural Language Processing

Test Data

Applied Machine Learning

Classification And Regression Tree (CART)

Python Programming

Scikit Learn (Machine Learning Library)

Text Mining

There are 4 modules in this course

By the end of this course you will be familiar with diagnostic techniques that allow you to evaluate and compare classifiers, as well as performance measures that can be used in different regression and classification scenarios. We will also study the training/validation/test pipeline, which can be used to ensure that the models you develop will generalize well to new (or "unseen") data.

Week 2: Codebases, Regularization, and Evaluating a Model

Week 3: Validation and Pipelines

Final Project

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