Answer
Supervised: Linear/Logistic Regression, SVM, Trees, RF.
Unsupervised: K-means, PCA.
Ensemble: Boosting, Bagging.
Unsupervised: K-means, PCA.
Ensemble: Boosting, Bagging.
Quick Answer
Common ML algorithms in Python: Linear/Logistic Regression (baseline, interpretable). Decision Trees and Random Forest (ensemble, handles non-linearity). Gradient Boosting (XGBoost, LightGBM - usually best for tabular data). SVM for small-medium datasets. K-Means for clustering. Neural networks (PyTorch, TensorFlow) for images and NLP. Start simple, add complexity only when needed.
S
SugharaIQ Editorial Team
Verified Answer
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Source: SugharaIQ