Competencies: AI & ML > Classical Machine Learning

Supervised Learning Fundamentals

Training data, labels, overfitting/underfitting, train/val/test splits, cross-validation.

Critical

Data Scientist, ML Engineer

Linear Regression

Ordinary least squares, polynomial features, regularization, assumptions, interpretation.

Critical

Data Scientist, Analyst

Logistic Regression

Binary classification, sigmoid, decision boundary, multiclass (softmax), coefficients.

Critical

Data Scientist, ML Engineer

Decision Trees

Splitting criteria (Gini, entropy), pruning, feature importance, interpretability.

High

Data Scientist, ML Engineer

Ensemble Methods

Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost, bagging vs boosting.

Critical

Data Scientist, ML Engineer

Support Vector Machines

Margin maximization, kernel trick, RBF kernel, hyperparameter tuning, SVR.

Medium

Data Scientist, ML Engineer

K-Nearest Neighbors

Distance metrics, k selection, curse of dimensionality, efficient search (KD-tree).

Medium

Data Scientist

Clustering

K-Means, hierarchical, DBSCAN, silhouette score, elbow method, cluster validation.

High

Data Scientist, Analyst

Dimensionality Reduction

PCA, t-SNE, UMAP, feature selection, variance explained, visualization.

High

Data Scientist, ML Engineer

scikit-learn

API patterns (fit/transform/predict), pipelines, preprocessing, model selection, GridSearchCV.

Critical

Data Scientist, ML Engineer

Feature Engineering

Feature creation, encoding categoricals, scaling, handling missing data, feature stores.

Critical

Data Scientist, ML Engineer

No personal status recorded

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