| 1 - Introduction.mp4 | 10.1 MB | ||
| 10 - Checklist of ML Test strategy Template.mp4 | 12.3 MB | ||
| 11 - Choosing the Right Metrics for testing.mp4 | 10.7 MB | ||
| 12 - Introduction to Test Automation in ML.mp4 | 18.6 MB | ||
| 13 - Challenges in ML Automation Testing.mp4 | 12.6 MB | ||
| 14 - Automated Testing pipeline.mp4 | 10.8 MB | ||
| 15 - Best Practices for Test Automation in ML.mp4 | 10.4 MB | ||
| 16 - Introduction to Data Testing in ML.mp4 | 21 MB | ||
| 17 - Writing Automated Tests for Data Quality.mp4 | 9.2 MB | ||
| 18 - Hands on Exercise Writing test scripts for missing data duplicates outliers.mp4 | 36.1 MB | ||
| 19 - Ensuring Consistency in Data Distribution.mp4 | 9.7 MB | ||
| 2 - What is Machine Learning.mp4 | 19.8 MB | ||
| 20 - Hands on Exercise Automation test scripts for Data Distribution.mp4 | 43.6 MB | ||
| 21 - Hands on Exercise Automation test scripts for Data Drift.mp4 | 48 MB | ||
| 22 - Hands on Exercise Automation test scripts for Feature engineer.mp4 | 16.9 MB | ||
| 23 - Tools for Data Testing.mp4 | 9.7 MB | ||
| 24 - Setting up Robust Testing Framework.mp4 | 21.3 MB | ||
| 25 - Key Concepts Overfitting Underfitting BiasVariance Tradeoff.mp4 | 40.9 MB | ||
| 26 - Regression Metrics MAE MSE RMSE R.mp4 | 16.6 MB | ||
| 27 - Classification Metrics Confusion Matrix Accuracy Precision Recall F1Score.mp4 | 29.9 MB | ||
| 28 - Classification Metrics ROC and AUC.mp4 | 18.1 MB | ||
| 29 - Evaluation Metrics for Unsupervised Learning Silhouette score and Elbow method.mp4 | 51.5 MB | ||
| 3 - Various types of ML.mp4 | 27.6 MB | ||
| 30 - Writing Automated Tests for Model Evaluation.mp4 | 20 MB | ||
| 31 - Handson Exercise Training Evaluating a Model Using ScikitLearn.mp4 | 8.9 MB | ||
| 32 - Hands on Exercise Automated tests for Classification Metrics.mp4 | 14.4 MB | ||
| 33 - Hands on Exercise Automated tests for Regression Metrics.mp4 | 10.8 MB | ||
| 34 - Hands on Exercise Automated tests for Clustering Metrics.mp4 | 11.4 MB | ||
| 35 - Organising the tests.mp4 | 11.7 MB | ||
| 36 - Using TFMA for TensorFlow Models.mp4 | 11.2 MB | ||
| 37 - The concept of Generalization in ML models.mp4 | 11.1 MB | ||
| 38 - CrossValidation Explained.mp4 | 4.3 MB | ||
| 39 - Understanding kfold crossvalidation and its importance.mp4 | 6.1 MB | ||
| 4 - The ML workflow Data collection preprocessing model training and evaluation.mp4 | 18.3 MB | ||
| 40 - Hands on Automating CrossValidation Testing.mp4 | 13.1 MB | ||
| 41 - Hands on Evaluating model generalization across multiple data splits.mp4 | 15.4 MB | ||
| 42 - Tools for CrossValidation Using Scikitlearn for automated crossvalidation.mp4 | 13.6 MB | ||
| 43 - Introduction to Robustness Testing.mp4 | 25.7 MB | ||
| 44 - Automating Edge Case and Noise Testing.mp4 | 6.3 MB | ||
| 45 - Hands on Writing automated tests for Edge cases in data.mp4 | 20.7 MB | ||
| 46 - Hands on Writing automated tests for Noise in data.mp4 | 12.1 MB | ||
| 47 - Hands on Generating adversarial examples for testing model robustness.mp4 | 30 MB | ||
| 48 - Tools for Robustness Testing.mp4 | 12.5 MB | ||
| 49 - Understanding Fairness and Bias in ML.mp4 | 12.4 MB | ||
| 5 - Understanding ML testing.mp4 | 8.7 MB | ||
| 50 - Detecting and mitigating bias in ML.mp4 | 10.1 MB | ||
| 51 - Automated Tests for Fairness and Bias.mp4 | 8.9 MB | ||
| 52 - Tools for Fairness Testing.mp4 | 19.7 MB | ||
| 53 - Hands on Exercise Automating Fairness testing using AIF 360.mp4 | 24.1 MB | ||
| 54 - Hands on Exercise Automated Fairness Test Script using Fairlearn.mp4 | 24 MB | ||
| 55 - Integrating Fairness tests into CICD pipeline.mp4 | 8.7 MB | ||
| 56 - Introduction to Deployment Testing.mp4 | 19.7 MB | ||
| 57 - Introduction to Automated Deployment Testing.mp4 | 5.7 MB | ||
| 58 - Hands on Writing Automated API Tests with Pytest.mp4 | 5.4 MB | ||
| 59 - Tools for Deployment Testing.mp4 | 4.1 MB | ||
| 6 - ML testing as a Process.mp4 | 14.4 MB | ||
| 60 - Hands on Postman API Test Automation.mp4 | 5.4 MB | ||
| 61 - Hands on Performance Testing ML Apis with Locust.mp4 | 12.8 MB | ||
| 62 - Hands on Security Testing for Unauthorized Access Injection Attacks.mp4 | 7.2 MB | ||
| 63 - Introduction to Model Monitoring.mp4 | 8.6 MB | ||
| 64 - Automating Monitoring and Maintenance.mp4 | 6.2 MB | ||
| 65 - Tools for Monitoring.mp4 | 7.8 MB | ||
| 66 - Hands on Detecting Data drift with Evidently AI.mp4 | 12.9 MB | ||
| 67 - Setting Up Alerts Triggers with Prometheus Grafana.mp4 | 9.2 MB | ||
| 68 - E2E Automated Monitoring and Alerts for ML Models.mp4 | 22.7 MB | ||
| 69 - Key TakeAways.mp4 | 7.6 MB | ||
| 7 - Key differences between Traditional and ML testing.mp4 | 7.8 MB | ||
| 8 - Challenges with ML Testing.mp4 | 18.8 MB | ||
| 9 - Introduction to Test Strategy in ML.mp4 | 35.2 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 71 total files | |||
The Ultimate Guide For Automated Machine Learning Testing
https://WebToolTip.com
Published 2/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.08 GB | Duration: 4h 36m
Hands-on ML Testing & Automation: Build Reliable Models with Ready-to-Use Scripts & Real-World Testing Frameworks!
What you'll learn
Gain hands-on experience with ready-to-use ML testing scripts and automation frameworks to confidently apply testing techniques in real projects.
Apply ML testing concepts through real-world exercises, coding challenges, and practical scripts designed to accelerate your learning. Learn to automate ML dat
Learn to automate ML data validation, model evaluation, and API testing with industry-relevant tools and hands-on implementation.
Build expertise in ML test automation with step-by-step guidance, covering data quality, model performance, bias detection, and deployment testing.
Requirements
Basic Understanding of Machine Learning – Familiarity with ML concepts like training models, evaluation, and common algorithms is helpful.
Python Programming Skills – Knowledge of Python and basic scripting is required for hands-on exercises.
Familiarity with ML Libraries - like Scikit-learn, TensorFlow, or PyTorch is beneficial.
Basic Understanding of Software Testing – Awareness of testing concepts (unit testing, validation) will be useful but not mandatory And Interest in ML Automation – No prior ML testing experience is needed, but curiosity about automating ML workflows will be helpful.
| torrent name | size | uploader | age | seed | leech |
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| 2 GB | freecoursewb | 1 week | 18 | 13 | |
| 2.4 GB | freecoursewb | 3 weeks | 1 | 35 | |
| 1.2 GB | freecoursewb | 3 weeks | 3 | 9 | |
| 775.8 MB | freecoursewb | 3 weeks | 1 | 5 | |
| 1.3 GB | freecoursewb | 3 weeks | 14 | 5 |
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