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Data Science

Machine Learning Basics

Data Science 243 views Nov 14, 2025 1 min read

Introduction

Machine Learning (ML) is a branch of Artificial Intelligence where computers learn patterns from data and make predictions without being explicitly programmed.

1. Types of Machine Learning

  • Supervised Learning – Labeled data (e.g., Classification, Regression)
  • Unsupervised Learning – Unlabeled data (e.g., Clustering)
  • Reinforcement Learning – Reward-based learning

2. Machine Learning Workflow

  1. Collect Data
  2. Prepare Data
  3. Feature Engineering
  4. Model Selection
  5. Training
  6. Testing
  7. Evaluation
  8. Deployment

3. Features and Labels


X = df.drop("target", axis=1)
y = df["target"]

4. Train/Test Split


from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

5. Model Evaluation

  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • Confusion Matrix

Conclusion

This tutorial covers the foundation needed for building Machine Learning models and prepares you for upcoming algorithms.

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