admin
Statistical Learning Framework, Empirical Minimization Framework, PAC Learning, Version Spaces, Find-S Algorithm, Candidate Elimination Algorithm, VC-Dimension, Fundamental Theorem of PAC Learning.
Linear Regression, Linear Regression-Cost Function and Gradient Descent, Multivariate Linear Regression, Gradient Descent for Multiple Variables, Polynomial Regression, Logistic Regression.
Hypothesis Representation, Logistic Regression-Decision Boundary-Cost Function and Gradient Descent-Advanced Optimization-Multiple Classification.
Ensemble Learning, Error Correcting Output Codes, Boosting Weak Learnability, Adaboost Algorithm, Stacking, Gradient Descent Algorithm, Subgradient Descent, Stochastic Gradient Descent, SGD Variants, Kernels, Kernels Trick.
Support Vector Machines, Large Margin Intuitions, Margin and Hard SVM, Soft SVM and Norm Regularization, Optimality Conditions and Support Vectors, Implementing Soft SVM and SGD.
Decision Trees, Decision Tree Pruning, Classification Tree, Regression Trees, Random Forest Algorithm,K-Nearest Neighbor Algorithm, Nearest Neighbor Analysis, Naive-Bayes Algorithm.
Yes, this test is designed and created by an expert team of machine learning professionals with extensive experience in the field. While no test can guarantee specific outcomes, this comprehensive practice test covers all essential topics, helping you thoroughly prepare and improve your understanding of machine learning concepts.
