𝟔 𝐂𝐨𝐫𝐞 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐘𝐨𝐮 𝐌𝐮𝐬𝐭 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 of ML:
📈 Linear Regression —
Your starting point. Simple, interpretable, and powerful for trend forecasting. Remember: always choose the simplest model (Occam's Razor applies!).
🌳 Decision Trees—
The most explainable ML model. Can handle both classification and regression. Always prune to avoid overfitting.
📍 k-Nearest Neighbor —
Lazy learning at its finest. Perfect for large datasets, but beware the *curse of dimensionality*.
⚔️ Support Vector Machine —
One of the best off-the-shelf algorithms. The kernel trick makes it surprisingly powerful for non-linear problems.
🧠 Neural Networks —
Biologically inspired and incredibly flexible. Mastering backpropagation is non-negotiable.
🔵 Clustering (k-Means) —
Unsupervised learning that groups data by similarity. No labels needed!
The Golden Rule across ALL algorithms?
➡️ Preprocessing is everything. Handle missing values, remove outliers, normalize your data.
