The MaxX Academy

Unlearn and then Learn — Welcome to The Maxx Academy, a channel dedicated to making complex academic concepts easy to understand, smooth to follow, and deeply insightful. Our mission is to help you build a strong foundation in Computer Science & Engineering, especially for competitive exams like GATE, UGC NET, and more.

Join us on a journey where we break down, rebuild, and elevate your understanding — one concept at a time.

P-1: Practice Set l machine learning
P-1: Practice Set l machine learning
L-11.2: Practice Problem on Minimization of DFA | Theory of Computation
L-11.2: Practice Problem on Minimization of DFA | Theory of Computation
L-11.1 Minimization of DFA (Equivalence Theorem) | Theory of Computation
L-11.1 Minimization of DFA (Equivalence Theorem) | Theory of Computation
L-19: Cross- Validation and Classification Accuracy | Machine Learning
L-19: Cross- Validation and Classification Accuracy | Machine Learning
L-18: Bias-Variance Tradeoff | Machine Learning
L-18: Bias-Variance Tradeoff | Machine Learning
L-17: Support Vector Machine (SVM) | Machine Learning
L-17: Support Vector Machine (SVM) | Machine Learning
L-16: Linear Discriminant Analysis (LDA) | Machine Learning
L-16: Linear Discriminant Analysis (LDA) | Machine Learning
L-15: Naive Bayes Classifier | Machine Learning
L-15: Naive Bayes Classifier | Machine Learning
L-10.1: Conversion Of NFA to DFA | Theory of Computation
L-10.1: Conversion Of NFA to DFA | Theory of Computation
L-10: Comparison Between NFA, DFA and ε-NFA (Epsilon-NFA) | Theory of Computation
L-10: Comparison Between NFA, DFA and ε-NFA (Epsilon-NFA) | Theory of Computation
L-9.1: ε-Closure Of State & Conversion of ε-NFA (Epsilon-NFA) To NFA | Theory of Automata
L-9.1: ε-Closure Of State & Conversion of ε-NFA (Epsilon-NFA) To NFA | Theory of Automata
L-14: Naive Bayes Classification | Machine Learning
L-14: Naive Bayes Classification | Machine Learning
L-13: Naive Bayes | Machine Learning
L-13: Naive Bayes | Machine Learning
L-12: Ridge and Lasso Regression | Machine Learning
L-12: Ridge and Lasso Regression | Machine Learning
L-11: Exponential Family, Likelihood Estimation, Maximum Likelihood Estimation | Machine Learning
L-11: Exponential Family, Likelihood Estimation, Maximum Likelihood Estimation | Machine Learning
L-10: Logistic Regression | Machine Learning
L-10: Logistic Regression | Machine Learning
L-9: Multiple Linear Regression | Machine Leaning
L-9: Multiple Linear Regression | Machine Leaning
L-8: Supervised Learning | Type of Supervised Learning | Simple Linear Regression | Machine Learning
L-8: Supervised Learning | Type of Supervised Learning | Simple Linear Regression | Machine Learning
L-7: Classification | Training & Testing Split | Overfitting vs Underfitting | Machine Learning
L-7: Classification | Training & Testing Split | Overfitting vs Underfitting | Machine Learning
L-6: Data Science vs Machine Learning vs Deep Learning | Types of Learning in ML | Machine Learning
L-6: Data Science vs Machine Learning vs Deep Learning | Types of Learning in ML | Machine Learning
L-5: Machine Learning Approaches Overview | Machine Learning
L-5: Machine Learning Approaches Overview | Machine Learning
L-4.3: Loss Function for Regression and Classification | Machine Learning
L-4.3: Loss Function for Regression and Classification | Machine Learning
L-4.2: Problem Solving on Performance Metric | Confusion Matrix Example-2 | Machine Learning
L-4.2: Problem Solving on Performance Metric | Confusion Matrix Example-2 | Machine Learning
L-4.1: Problem Solving on Performance Metric | Confusion Matrix Example-1 | Machine Learning
L-4.1: Problem Solving on Performance Metric | Confusion Matrix Example-1 | Machine Learning
L-9: Introduction To ε-NFA (Epsilon-NFA) with Formal Definition | Theory of Computation
L-9: Introduction To ε-NFA (Epsilon-NFA) with Formal Definition | Theory of Computation
L-8.3: Problem Solving On NFA (Practice Problems) | Theory of Computation
L-8.3: Problem Solving On NFA (Practice Problems) | Theory of Computation
L-8.2: Problem Solving On NFA (Practice Problems) | Theory of Computation
L-8.2: Problem Solving On NFA (Practice Problems) | Theory of Computation
L-8.1: Formal Definition of NFA | String & Language Accepted by NFA | Theory of Computation
L-8.1: Formal Definition of NFA | String & Language Accepted by NFA | Theory of Computation
L-4: Performance Metric Overview (Classification) | Machine Learning
L-4: Performance Metric Overview (Classification) | Machine Learning
L-3: History of ML | Machine Learning Life Cycle | Machine Learning
L-3: History of ML | Machine Learning Life Cycle | Machine Learning