Guide to Use Machine Learning Algorithms | IABAC

Guide to Use Machine Learning Algorithms | IABAC

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  Guide to UseML Algorithms iabac.org‌
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  Understanding the Basics SupervisedLearning: Uses labeled data for prediction.‌ Unsupervised Learning: Finds patterns in unlabeled data.‌ Reinforcement Learning: Learns by trial and error for‌ ‌ decision-making tasks.‌ iabac.org‌ Machine Learning (ML) automates predictions and insights from data.‌ Types of ML:‌ Importance: Automates predictions and insights‌
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  Collect relevant datasetsfrom reliable sources.‌ Clean and preprocess data: remove duplicates, handle missing values, normalize features.‌ Split data into training, validation, and test sets.‌ Feature engineering enhances model performance.‌ Data Preparation iabac.org‌
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  Choosing and TrainingAlgorithms Algorithm Selection:‌ Regression: Linear Regression, Decision Trees‌ Classification: Logistic Regression, Random Forest, SVM‌ Clustering: K-Means, DBSCAN‌ Train models using training data.‌ Hyperparameter tuning improves model performance.‌ Evaluate using metrics: Accuracy, Precision, Recall, F1-score, RMSE.‌ iabac.org‌
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  Choosing and TrainingAlgorithms Algorithm Selection:‌ Regression: Linear Regression, Decision Trees‌ Classification: Logistic Regression, Random Forest, SVM‌ Clustering: K-Means, DBSCAN‌ Train models using training data.‌ Hyperparameter tuning improves model performance.‌ Evaluate using metrics: Accuracy, Precision, Recall, F1-score, RMSE.‌ iabac.org‌
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  Deploy models intoapplications or pipelines.‌ Monitor model performance regularly.‌ Retrain with new data for continuous improvement.‌ Use optimization techniques: hyperparameter tuning, feature selection, dimensionality reduction.‌ Deployment & Optimization iabac.org‌
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  Thank You‌ visit: www.iabac.org‌ iabac.org‌