Guide to Use Machine Learning Algorithms | IABAC

1. Guide to Use ML Algorithms iabac.org

2. Understanding the Basics Machine Learning (ML) automates predictions and insights from data. Types of ML: Supervised Learning: Uses labeled data for prediction. Unsupervised Learning: Finds patterns in unlabeled data. Reinforcement Learning: Learns by trial and error for decision-making tasks. Importance: Automates predictions and insights iabac.org

3. Data Preparation Collect relevant datasets from 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. iabac.org

4. Choosing and Training Algorithms 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

5. Choosing and Training Algorithms 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

6. Deployment & Optimization Deploy models into applications or pipelines. Monitor model performance regularly. Retrain with new data for continuous improvement. Use optimization techniques: hyperparameter tuning, feature selection, dimensionality reduction. iabac.org

7. Thank You visit: www.iabac.org iabac.org