AI/ML Byte-Sized Series: Machine Learning Model Optimization
- Created By raju2006
- Last Updated December 1st, 2023
- Overview
- Prerequisite
- Audience
- Audience
- Curriculum
Description:
A short session indented to get started with techniques to optimize machine learning model performance.
Setup:
Because this is an abbreviated session, attendees MUST install Anaconda software https://www.anaconda.com/ and have a basic understanding of using Jupyter Notebook.
Course Code/Duration:
BDT104 / 90 Minutes
Learning Objectives:
Learn about what are hyper parameters and how to tune them. We will then build a model and tune the parameters:
- Use K-Fold to create diverse test buckets while building the model.
- Perform grid search to find the best parameters.
Training material provided: Yes (Digital format)
- Learn basic understanding of python language, pandas library and understanding of how to use Juypter Notebook. Also, understanding how to build either a classification models or regression models.
- This session is designed for anyone who is familiar with machine learning model development. Understanding of building Classification and/or Regression models will be helpful.
- This session is designed for anyone who is familiar with machine learning model development. Understanding of building Classification and/or Regression models will be helpful.
Course Outline:
- ML Models
- Classification
- Regression
- Hyperparameters
- Optimization of a Model
The curriculum is empty
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