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Model Evaluation
Master the art of evaluating machine learning models using practical metrics, validation techniques, and error analysis.Preview Model Evaluation course
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Model Evaluation is a specialized course focused on one of the most critical yet often overlooked stages of the machine learning pipeline: assessing how well your model performs and why. This course teaches you how to go beyond accuracy and truly understand the strengths and weaknesses of your models using a range of evaluation metrics and validation strategies.
You'll learn the difference between performance on training vs. test data, how to interpret confusion matrices, precision-recall trade-offs, ROC curves, and techniques like cross-validation and bootstrapping. Whether you're dealing with regression, classification, or clustering models, this course will equip you with the tools and mindset to evaluate models with confidence.
What You Will Gain
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Deep understanding of key evaluation metrics for various ML tasks
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Ability to choose the right metric for different use cases
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Experience in diagnosing overfitting, underfitting, and bias
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Mastery of validation strategies like k-fold CV and stratified sampling
Who This Course Is For
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Data Scientists fine-tuning models for real-world use
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ML Engineers evaluating models in production
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Students and professionals preparing for ML interviews
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Researchers validating academic model performance
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Anyone aiming to improve model performance and reliability
How to Use This Course Effectively
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Work through regression, classification, and clustering sections in order
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Use sample datasets to compute and visualize metrics
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Practice interpreting results from confusion matrices and ROC curves
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Track model performance over time to identify degradation
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Compare multiple models using statistical tests and visual diagnostics
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Treat the final project like a real client report with detailed analysis
Why Learn Model Evaluation?
Even the best-trained models are useless without proper evaluation. Choosing the wrong metric can lead to poor decisions, bias, or even model failure in production. Learning how to evaluate models rigorously is essential for delivering reliable, trustworthy, and high-performing machine learning solutions.
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
By the end of this course, you will be able to:
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Choose the right evaluation metric for different machine learning problems
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Perform error analysis to understand model shortcomings
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Use cross-validation, bootstrapping, and holdout sets effectively
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Interpret precision, recall, F1-score, ROC-AUC, and RMSE
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Compare models using statistical metrics and visualizations
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Present model evaluation results in a clear and meaningful way
Course Syllabus
Module 1: Introduction to Model Evaluation
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Why model evaluation matters
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Training vs. testing performance
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Overview of metrics and techniques
Module 2: Classification Metrics
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Accuracy, precision, recall, F1-score
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Confusion matrix interpretation
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ROC-AUC, PR curves, and threshold tuning
Module 3: Regression Metrics
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Mean Absolute Error (MAE), Mean Squared Error (MSE)
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Root Mean Squared Error (RMSE)
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R² score and adjusted R²
Module 4: Evaluation for Clustering and Anomaly Detection
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Silhouette score, Davies-Bouldin Index
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Precision at k, anomaly scoring methods
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Handling imbalanced datasets
Module 5: Validation Strategies
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Holdout and train/test split
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k-fold and stratified cross-validation
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Bootstrap methods
Module 6: Visual Evaluation Techniques
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ROC and PR curves
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Residual plots
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Lift and gain charts
Module 7: Statistical Comparison of Models
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Hypothesis testing for models
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Paired t-test, McNemar's test
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A/B testing for ML experiments
Module 8: Final Project
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Evaluate multiple models on a real-world dataset
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Use visual and quantitative metrics
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Present a report with findings and recommendations
Upon successful completion of this course, learners will receive a Certificate of Completion from Uplatz, verifying their expertise in evaluating machine learning models and validating model performance using industry-standard techniques.
This course prepares you for roles such as:
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Machine Learning Engineer
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Model Validation Analyst
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Data Scientist
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AI Research Assistant
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ML QA Specialist
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What is the difference between precision and recall?
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When would you use ROC-AUC instead of accuracy?
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How do you interpret a confusion matrix?
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What are some common metrics used in regression model evaluation?
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How does k-fold cross-validation work and why is it useful?
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What is the R² score and what are its limitations?
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How do you evaluate a model on imbalanced data?
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What’s the purpose of using a validation set separate from the test set?
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Explain the concept of overfitting and how to detect it through evaluation.
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How would you compare two models statistically to select the better one?