Скачать или смотреть AI and Machine Learning Basics: How Better Data Improves Accuracy, Precision, Recall

Data is the real engine behind AI. In this short beginner-friendly lesson, you’ll learn how training data quality, quantity, and diversity directly affect machine learning performance, bias, and trustworthiness.

We cover the foundations of supervised learning using a simple “apple recognition” example, then connect it to real-world evaluation using the confusion matrix, accuracy, precision, and recall (including why recall is critical in healthcare).

What you’ll learn

AI vs Machine Learning vs Deep Learning (quick recap)

What training data is, features vs labels

Why diverse data improves generalisation and reduces bias

Underfitting vs overfitting (and how to fix them)

Model evaluation: confusion matrix, accuracy, precision, recall

Responsible AI: ethics, transparency, and bias awareness

Chapters

00:00 Intro
01:00 Recap: AI, ML, deep learning
02:00 Data as the cornerstone of AI
03:00 Training data basics (features and labels)
04:00 Knowledge check: why diversity matters
05:00 Underfitting (and how to fix it)
06:00 Evaluation basics: confusion matrix, accuracy, precision, recall
07:00 Summary + responsible AI
07:50 Thanks

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#MachineLearning #ArtificialIntelligence #DataScience