how to work supervised machine learning and its application and evaluation metrics and also overfitting data quality

In this video, I revised my Machine Learning concepts. What is Machine Learning? Why do we use Machine Learning? Steps to perform Machine Learning Different Machine Learning models Different Evaluation Metrics Challenges of ML: Overfitting and Data Quality

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I learned from this lecture about key aspects, types, common algorithms, applications, and challenges.

I learned in this video key aspects (1. Labeled Data, 2. Model Traning, 3. Prediction, 4. Evaluation) and types of supervised machine learning (Regression, Classification) Common algorithms are (linear regression, logistic regression, decision tree, support vector machine, random forest, and gradient boost) then Application and Challenges (Overfitting and Data Quality).

This is about the review of supervised machine learning topics like definition, key aspects, types, common algorithms, applications, and challenges of Supervised Machine Learning