This course is a dedicated test series designed to rigorously assess and solidify your foundational knowledge in Machine Learning. It is structured around multiple comprehensive quizzes that cover the essential theoretical and mathematical concepts required before moving to advanced ML applications or interviewing for ML roles.

Why a Test Series?

Unlike traditional lecture-based courses, this series forces active recall and critical thinking. Each test component is carefully curated to mimic the types of theoretical and conceptual questions frequently encountered in job interviews or high-stakes academic exams. This immediate feedback loop is crucial for identifying knowledge gaps efficiently.

Key Assessment Areas Covered

The tests are segmented into critical domains, ensuring balanced coverage:

1. Core Algorithms: Linear Regression, Logistic Regression, Decision Trees, K-Means, SVM, Naive Bayes.

2. Mathematical Foundations: Cost Functions, Gradient Descent, and Basic Calculus concepts relevant to optimization.

3. Model Evaluation: Precision, Recall, F1-Score, Confusion Matrices, ROC Curves, and Cross-Validation Techniques.

4. Model Theory: Bias-Variance Tradeoff, Regularization (L1, L2), Overfitting, Underfitting, and Data Preprocessing Techniques.

By the end of this series, you will not only know the answers but also also understand the underlying principles, ensuring a robust and durable foundation in Machine Learning.Critically analyze and interpret model performance based on various cross-validation techniques and results.