Meaningful Predictive Modeling
- Offered byCoursera
Meaningful Predictive Modeling at Coursera Overview
Duration | 9 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Meaningful Predictive Modeling at Coursera Highlights
- This Course Plus the Full Specialization.
- Shareable Certificates.
- Graded Programming Assignments.
Meaningful Predictive Modeling at Coursera Course details
- This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"? Or how do we decide which of two regression algorithms is better?
- 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.
Meaningful Predictive Modeling at Coursera Curriculum
Week 1: Diagnostics for Data
Introduction to Course 3: Meaningful Predictive Modeling
Motivation Behind the MSE
Regression Diagnostics: MSE and R²
Over- and Under-Fitting
Classification Diagnostics: Accuracy and Error
Classification Diagnostics: Precision and Recall
Syllabus
Setting Up Your System
(Optional) Additional Resources and Recommended Readings
Course Materials
Review: Regression Diagnostics
Review: Classification Diagnostics
Diagnostics for Data
Week 2: Codebases, Regularization, and Evaluating a Model
Setting Up a Codebase for Evaluation and Validation
Model Complexity and Regularization
Adding a Regularizer to our Model, and Evaluating the Regularized Model
Evaluating Classifiers for Ranking
Review: Setting Up a Codebase
Review: Regularization
Review: Evaluating a Model
Codebases, Regularization, and Evaluating a Model
Week 3: Validation and Pipelines
Validation
?Theorems? About Training, Testing, and Validation
Implementing a Regularization Pipeline in Python
Guidelines on the Implementation of Predictive Pipelines
Review: Validation
Review: Predictive Pipelines
Predictive Pipelines
Final Project
Project Description
Where to Find Datasets
Meaningful Predictive Modeling at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
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