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Confused about supervised and unsupervised learning? This guide explains the key differences, real-world applications, advantages, and how these machine learning techniques power modern AI systems.
The End-to-End Data Science Workflow explains how raw data is transformed into intelligent, real-world solutions. From defining business objectives and collecting data to preprocessing, model training, evaluation, and deployment, this guide covers every stage of a complete data science project lifecycle. Whether you are a beginner or an aspiring professional, understanding this structured workflow helps you build scalable, production-ready machine learning models that deliver measurable business value.
