Byte-Sized ML Basic Series: Machine Learning Model Optimization
- Created By raju2006
- Last Updated December 12th, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
Let's dive into this short session to understand and review techniques which help in optimizing machine learning model performances.
Course Code/Duration:
BDT183 / 90 minutes.
Learning Objectives:
- Learn what hyperparameters are and how to tune them to improve model performance.
- Discuss Hyperparameters for various machine learning algorithm
- Learning how to evaluate model performance:
- Discuss k-fold cross-validation for measuring model performance.
- Discuss grid search to find the hyperparameters that optimize model performance.
- No prior technical knowledge.
- This session is for learners who would like to become familiar with the machine learning process.
- This session is for learners who would like to become familiar with the machine learning process.
Course Outline:
- Introduction to Machine Learning Model Optimization
- Hyperparameters for Various Machine Learning Algorithms
- Tuning Hyperparameters to Improve Model Performance
- Evaluating Model Performance Using K-Fold Cross-Validation
- Evaluating Model Performance Using Grid Search
Training Material Provided: Yes (Digital format)
The curriculum is empty
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