📘 Machine Learning Basics and Terminologies

Welcome to the Machine Learning Basics and Terminologies repository! This repository is designed for beginners and enthusiasts who want to understand the fundamental concepts of Machine Learning, key differences between related fields, and essential terminologies.

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📚 Contents

1. AI vs ML vs DL
2. Traditional Programming vs Machine Learning
3. Machine Learning Lifecycle
4. Types of Machine Learning - Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning
5. [Common Machine Learning Terminologies]
6. Model Performance Metrics
7. [Popular Machine Learning Algorithms]

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1. AI vs ML vs DL

Data Analytics

• Artificial Intelligence (AI): A broad field aiming to create machines that can simulate human intelligence.
• Machine Learning (ML): A subset of AI that allows systems to learn and improve from experience.
• Deep Learning (DL): A subset of ML focused on neural networks with many layers (deep architectures).

🔁 AI ⊃ ML ⊃ DL

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2. Introduction to Machine Learning

Machine Learning is the science of getting computers to learn patterns from data and make decisions or predictions. It is used in various domains such as healthcare, finance, marketing, and more.

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3. Traditional Programming vs Machine Learning

Traditional Programming	Machine Learning
Rules + Data → Output	Data + Output → Rules
Explicit logic written	Model learns patterns
Deterministic	Probabilistic

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4. Machine Learning Lifecycle

1. Problem Definition
2. Data Collection
3. Data Preprocessing
4. Exploratory Data Analysis
5. Model Selection
6. Training
7. Evaluation
8. Deployment
9. Monitoring and Maintenance

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5. Types of Machine Learning

• Supervised Learning
• Unsupervised Learning
• Semi-Supervised Learning
• Reinforcement Learning

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6. Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning

• Supervised Learning: Labeled data used (e.g., classification, regression)
• Unsupervised Learning: No labels (e.g., clustering, dimensionality reduction)
• Semi-Supervised Learning: Few labeled and many unlabeled data points
• Reinforcement Learning: Agent learns by interacting with an environment via rewards/punishments

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7. Supervised Learning: Regression vs Classification

• Regression: Predicts continuous values (e.g., house price)
• Classification: Predicts discrete labels (e.g., spam or not)

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8. Common Machine Learning Terminologies

• Feature: Input variable
• Label: Output variable
• Model: Learned algorithm
• Training: Learning from data
• Testing: Evaluating performance
• Overfitting: Too complex, memorizes training data
• Underfitting: Too simple, misses patterns
• Bias & Variance: Errors due to assumptions vs fluctuations

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9. Model Performance Metrics

For Classification:

• Accuracy
• Precision
• Recall
• F1-Score
• ROC-AUC

For Regression:

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• R² Score

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10. Popular Machine Learning Algorithms

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Support Vector Machines (SVM)
• K-Nearest Neighbors (KNN)
• Naïve Bayes
• Gradient Boosting (e.g., XGBoost, LightGBM)
• K-Means Clustering (Unsupervised)
• Principal Component Analysis (PCA)

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📂 Repository Structure

📁 machine-learning-basics/
│
├── ai-vs-ml-vs-dl.md
├── introduction-to-ml.md
├── traditional-vs-ml.md
├── ml-lifecycle.md
├── types-of-ml.md
├── supervised-vs-unsupervised.md
├── regression-vs-classification.md
├── ml-terminology.md
├── performance-metrics.md
├── ml-algorithms.md
└── README.md

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✅ Contribution

Pull requests are welcome! Feel free to improve the content or suggest better examples.

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📧 Contact

Feel free to connect via GitHub Issues for any questions or suggestions.

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