Model Evaluation and Tuning Basics
Course provided by Model Institute of Engineering & Technology
5 modules
Explore the fundamentals of Artificial Intelligence & Machine Learning
4.5 Level NCrF
National Credit Framework
30 Hours
Flexible Learning
Beginner Level
No prior experience required
Nano Credic Course
01 Credit
Course Overview
This beginner-friendly course delivers a hands-on, tool-based introduction to model evaluation and performance tuning in machine learning. Through practical exercises using real model outputs and scikit-learn, learners master key evaluation metrics, confusion matrix diagnostics, ROC-AUC analysis, and hyperparameter tuning. The course empowers participants to critically assess model performance and make data-driven improvements.
Key Learning Highlights
Skill-building with confusion matrices, precision-recall, and ROC-AUC
Hands-on hyperparameter tuning using real model outputs
Diagnostic practice through performance metric analysis
Use of scikit-learn evaluation and tuning tools
Application of best practices for responsible AI model assessment
Tools & Platforms Used
Python
The core programming language for building and evaluating machine learning models.
Scikit-learn
Library for classification metrics, cross-validation, and hyperparameter tuning.
Microsoft Azure ML
Cloud platform for deploying, monitoring, and evaluating ML models at scale.
Jupyter Notebook
Interactive environment for executing code, visualizing results, and experimenting.
Matplotlib & Seaborn
Visualization libraries for plotting metrics, confusion matrices, and ROC curves.
Learning Outcome
By the end of this course, learners will be able to:
- Understand and apply key model evaluation metrics—accuracy, precision, recall, F1-score
- Interpret confusion matrices and ROC curves for binary and multi-class classification tasks
- Execute hyperparameter tuning strategies (grid search, random search) to boost model performance
- Utilize scikit-learn’s tools for robust evaluation and tuning on standard datasets
- Analyze and compare different model outcomes to identify the best-performing variant
Master the course with just 5 Modules
This course takes learners from the fundamentals of model evaluation to practical performance tuning techniques. Beginning with essential metrics like accuracy, precision, recall, and F1-score, participants progress to interpreting confusion matrices and ROC-AUC curves for deeper diagnostic insights. The journey continues with robust evaluation methods such as cross-validation, followed by hands-on hyperparameter tuning using scikit-learn. The course concludes with a real-world comparison project, where learners fine-tune and analyze multiple classifiers to determine the most effective model.
Classification Metrics
- Understand accuracy, precision, recall, and F1-score.
- Learn when to use each metric for different problem types.
- Identify the limitations and trade-offs of each metric.
Confusion Matrix & ROC-AUC
Interpret true positives, false positives, true negatives, and false negatives.
Analyze classification performance using ROC curves and AUC scores.
Detect patterns and errors in model predictions for better diagnostics.
Cross-Validation
Apply K-fold and stratified cross-validation methods.
Reduce bias and variance in performance estimates.
Understand the role of sampling in reliable model evaluation.
Hyperparameter Tuning
Implement grid search and random search techniques.
Optimize model parameters for improved performance.
Use scikit-learn tools for automated tuning workflows.
Model Comparison Assignment
Train and evaluate two classifiers on the same dataset.
Apply tuning methods to enhance both models.
Compare performance metrics to select the best model.
Roles
ML Tester
Model Evaluation Analyst
AI Performance Engineer
