Ml Workflow — Free Machine Learning Tutorial
Learn Ml Workflow in Machine Learning with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Ml Workflow in Machine Learning with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
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Ml Workflow in Machine Learning
The core ML workflow is: (1) Data collection and labeling, (2) Exploratory analysis to understand your data, (3) Preprocessing (cleaning, scaling, encoding), (4) Feature engineering (creating useful input variables), (5) Train/test split (reserve some data to test on), (6) Model training (learning patterns from training data), (7) Evaluation (measuring performance), (8) Hyperparameter tuning (optimizing knobs like learning rate), (9) Deployment.
Train/test split is critical: we split data into training (e.g. 80%) used to train the model, and testing (e.g. 20%) held out to evaluate. Without this split, a model can memorize training data and appear perfect but fail on new data. For time-series data, split chronologically (train on past, test on future). For imbalanced data (e.g. 95% class A, 5% class B), stratify the split to preserve class ratios.
Common practice: 70-80% train, 10-15% validation (for tuning), 10-15% test. Validation data helps choose the best hyperparameters without overfitting to the test set. If data is small (<1000 samples), use cross-validation: split into k folds, train k times (leaving one fold out each time), and average the scores.
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