Udemy - The Ultimate Guide For Automated Machine Learning Testing

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[ WebToolTip.com ] Udemy - The Ultimate Guide For Automated Machine Learning Testing
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction
    • 1 - Introduction.mp4 (10.1 MB)
    10 - Module 9 Monitoring Maintenance and Continuous Testing
    • 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)
    2 - Module 1 Basics of Machine Learning
    • 2 - What is Machine Learning.mp4 (19.8 MB)
    • 3 - Various types of ML.mp4 (27.6 MB)
    • 4 - The ML workflow Data collection preprocessing model training and evaluation.mp4 (18.3 MB)
    3 - Module 2 Basics of Machine learning Testing
    • 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)
    • 5 - Understanding ML testing.mp4 (8.7 MB)
    • 6 - ML testing as a Process.mp4 (14.4 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)
    4 - Module 3 Automated Data Testing
    • 16 - Introduction to Data Testing in ML.mp4 (21.0 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)
    • 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 (48.0 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)
    5 - Module 4 Model Evaluation and Automated Testing
    • 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)
    • 30 - Writing Automated Tests for Model Evaluation.mp4 (20.0 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)
    6 - Module 5 CrossValidation and Generalization Testing
    • 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)
    • 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)
    7 - Module 6 Robustness and Stress Testing
    • 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 (30.0 MB)
    • 48 - Tools for Robustness Testing.mp4 (12.5 MB)
    8 - Module 7 Fairness and Bias Testing
    • 49 - Understanding Fairness and Bias in ML.mp4 (12.4 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 (24.0 MB)
    • 55 - Integrating Fairness tests into CICD pipeline.mp4 (8.7 MB)
    9 - Module 8 Deployment and PostDeployment Testing
    • 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)
    • 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)
    • Bonus Resources.txt (0.1 KB)

Description

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.



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Udemy - The Ultimate Guide For Automated Machine Learning Testing

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1.1 GB
seeders:6
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Udemy - The Ultimate Guide For Automated Machine Learning Testing

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