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This infographic breaks down the four essential stages of how machine learning models learn u2014 from collecting and preparing raw data, to training algorithms, optimizing performance, and finally evaluating accuracy on unseen data.<br>Designed with a clean, minimal layout, it provides a quick and clear overview of the ML development lifecycle, making it ideal for students, professionals, and anyone exploring AI fundamentals.
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How Machine Learning Models Learn STEP 01 Data Collection & Preparation Raw data is collected from various sources and cleaned to remove noise. STEP 02 Model Training The algorithm learns patterns by analyzing labeled or unlabeled data. STEP 03 Model Optimization Hyperparameters are tuned to improve accuracy and performance. STEP 04 Testing & Evaluation The model is tested on unseen data to measure accuracy and reliability. www.hdatasystems.com