Guide to Use Machine Learning Algorithms | IABAC

This guide explains how to use machine learning algorithms, covering problem identification, data preparation, algorithm selection, model training, evaluation, and deployment. It provides a structured approach to build, optimize, and maintain effective ML models for real-world applications.

1. Guide to Use ML
Algorithms iabac.org‌

2. Understanding the Basics
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.‌
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Machine Learning (ML) automates predictions and insights from data.‌
Types of ML:‌
Importance: Automates predictions and insights‌

3. 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.‌
Data Preparation
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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.‌
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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.‌
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6. 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.‌
Deployment & Optimization
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7. Thank You‌ visit: www.iabac.org‌
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