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Data Model Validation in Machine Learning
Duration
4 Hours
Course
WEB-202009
Available Formats
Webinar
Course Description
Overview
In this webinar, we will follow the intuiting approach to explore the model validation.Complexity of the model influences its accuracy score and that can be explained by plotting the validation curves.
Similarly, the learning curves are plotted to examine how the model accuracy depends on the training set size.
Next, we consider a practical technique, Grid Search, for machine learning models validation.
Polynomial regression models are used to demonstrate validation with the Grid Search using the Python library.
Objectives
The main objectives of the webinar are the following,
- Learn the relationship between Model Learning and Model Validation
- Understand Validation Curves’ behavior depending on the Model Complexity
- Understand Learning Curves’ behavior depending on the Training Dataset Size
- Learn how to perform GridSearch for a Best Fit Model in Python
Audience
- Professionals who recently started working in data analysis
- Students who study Data Science (DS) and Machine Learning (ML)
- Professionals who would like to apply Machine Learning (ML) tools in their fields to drive business growth
Topics
- Types of Machine Learning
- The DS & ML Pipeline
- Model Learning as a Process
- Model Validation as a Process
- Selecting the Best Model – Complexity – Validation Curves
- Selecting the Best Model – Size of Training Dataset – Learning Curves
- Validation in Practice: Grid Search
- Grid Search for a Best Fit Model in Python
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title
Please take a moment to fill out this form. We will get back to you as soon as possible.
All fields marked with an asterisk (*) are mandatory.