Machine Learning Fundamentals

This exam validates your understanding of core Machine Learning concepts and your ability to apply ML in Python. The focus is on classic algorithms, data preparation and model evaluation.

You will be tested on topics such as:

• Basic ML concepts and types: supervised, unsupervised, reinforcement
• Data preparation and feature engineering with Pandas/NumPy
• Regression and prediction of numeric values
• Classification with algorithms such as Logistic Regression and Naive Bayes
• Tree-based models: Decision Trees and Random Forest
• Unsupervised learning: clustering (k-means) and dimensionality reduction (PCA)
• Model validation, bias-variance tradeoff and overfitting
• Hyperparameter tuning and model optimization