Why Data Quality Makes or Breaks Machine Learning скачать в хорошем качестве

Why Data Quality Makes or Breaks Machine Learning

Data quality is the foundation of every successful machine learning model. In this video, we break down the most common data quality issue, such as completeness, relevance, representativeness, uniqueness, validity, integrity, timeliness, and uniformity and explain why they matter in real-world ML systems. You’ll learn: What each data quality issue really means (in simple terms) How poor data quality leads to biased models, data leakage, and unreliable predictions High-level data curation techniques used to address these issues before training ML models Whether you’re a beginner or an experienced ML practitioner, this video will help you understand why clean, well-curated data is just as important as model selection and tuning. Perfect for data scientists, ML engineers, and anyone building machine learning systems.