Machine learning is filled with algorithms — but a few form the backbone of most real-world applications. Mastering them gives you a strong foundation for practical problem-solving.
Must-Know Algorithms
- Linear Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- K-Means Clustering
When to Use What
Each algorithm has strengths based on your data's size, noise, and feature complexity. Understanding the trade-offs is key to model selection.
Next Steps
Experiment with open datasets and visualize results to grasp their behavior under different conditions.