Machine Learning Quiz

ML Arena

Explore the topics below, then open the quiz page to attempt a 25-question random challenge from a 50-question bank.

Module 1 – Foundations of ML

Introduction to ML

  • Machine Learning vs. Traditional Programming
  • Learning Paradigms: supervised, semi-supervised, unsupervised, reinforcement learning

Parameter Estimation

  • Maximum Likelihood Estimation (MLE)
  • Maximum A Posteriori (MAP)
  • Bayesian formulation and posterior reasoning

Supervised Learning Foundations

  • Feature Representation and Problem Formulation
  • Role of loss functions and optimization

Regression

  • Linear regression with one variable
  • Linear regression with multiple variables
  • Solution using gradient descent and matrix method

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Classification and Logistic Regression

  • Regression vs classification
  • Binary, multi-class, and multi-label classification
  • Why linear regression is not used for classification
  • Sigmoid function, log-likelihood, and cross-entropy

Module 2 – Evaluation & Naïve Bayes

Classifier Evaluation & Naïve Bayes

  • Confusion Matrix — TP, TN, FP, FN; Type-I & Type-II Errors
  • Accuracy, Precision, Recall (Sensitivity)
  • F1-Score, ROC Curve & AUC
  • Naïve Bayes — Bayes' Theorem, independence assumption, Laplace Smoothing

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Start Module 2 Quiz Start Module 2 Quiz – Eval & Naïve Bayes Start Module 2 Crossword