<ul><li>Machine learning (ML) is the invisible engine behind various applications like Netflix, fraud-proof banks, and stock markets.</li><li>Building a top-tier ML pipeline requires a 6-step grind that anyone can learn, starting with data collection and ending with deployment.</li><li>Key steps include data cleaning to handle missing values, duplicates, encoding, and scaling.</li><li>The importance of exploratory data analysis (EDA) is highlighted, as it helps uncover outliers, trends, and feature cuts before model development.</li></ul>

The Genius Little Secret Behind Killer Machine Learning Pipelines (Plus Stats That’ll Leave You…

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