Machine Learning Basics

⏱ Estimated reading time: 1 min

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.