Machine Learning Model Evaluation & Validation

Dive deep into the critical world of Model Evaluation and Validation in Machine Learning with this comprehensive guide. Learn essential techniques to ensure your models perform well not just on training data, but also on new, unseen data.

This video covers:

• Understanding Training, Validation, and Test Sets

• Cross-Validation Techniques (K-Fold, LOOCV)

• Performance Metrics for Classification and Regression

• Detecting and Preventing Overfitting and Underfitting

Whether you're a beginner or looking to refine your skills, this video provides valuable insights into creating robust, reliable machine learning models. Learn how to effectively split your data, choose the right evaluation metrics, and implement strategies to optimize your model's performance.

Don't miss out on mastering these crucial skills that can set you apart in the field of AI and machine learning. Watch now to enhance your problem-solving capabilities and take your machine learning projects to the next level!

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OUTLINE:
00:00:00 Introduction to Model Evaluation and Validation
00:00:39 Training, Validation, and Test Sets
00:00:46 Understanding Different Data Splits
00:01:13 Importance of Data Splitting
00:01:37 Cross-Validation Techniques
00:01:42 What is Cross-Validation?
00:02:00 Types of Cross-Validation
00:02:29 Metrics for Model Performance Evaluation
00:02:39 Classification Metrics
00:03:07 Regression Metrics
00:03:34 Detecting Overfitting and Underfitting
00:03:39 Signs of Overfitting
00:03:53 Signs of Underfitting
00:04:06 Strategies to Avoid Overfitting and Underfitting
00:04:36 Summary
00:05:06 About the Course

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