Varunsaxena
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ML Mastery Notes – SVM, Ensemble Methods & Boosting
Topics Covered in the Handwritten ML Notebook 1. Support Vector Machine (SVM) - Hyperplane, Margin, Support Vectors - Kernels (Linear, Polynomial, RBF, Sigmoid) - SVC (Support Vector Classification) - SVR (Support Vector Regression) - Slack variables, ε-tube, optimization 2. Ensemble Learning - Bagging - Boosting - Stacking - Voting 3. Bagging Techniques - Bagging Classifier - Bagging Regressor - Random...
- Other
- • 24 pages •
Topics Covered in the Handwritten ML Notebook 1. Support Vector Machine (SVM) - Hyperplane, Margin, Support Vectors - Kernels (Linear, Polynomial, RBF, Sigmoid) - SVC (Support Vector Classification) - SVR (Support Vector Regression) - Slack variables, ε-tube, optimization 2. Ensemble Learning - Bagging - Boosting - Stacking - Voting 3. Bagging Techniques - Bagging Classifier - Bagging Regressor - Random...
Mastering Decision Trees: Handwritten Notes + Code (Beginner to Advanced)
Master Decision Trees with this complete handwritten notes pack covering both Classification and Regression. Inside, you’ll learn: • Decision Tree fundamentals (structure, splits, overfitting) • Entropy with formulas and worked examples • Gini Impurity and comparison with Entropy • Information Gain step-by-step calculation • Pruning techniques and common parameters • Decision Tree as Regressor • Variance Reduction with formula explanation • Ready-to-use Python code...
- Other
- • 6 pages •
Master Decision Trees with this complete handwritten notes pack covering both Classification and Regression. Inside, you’ll learn: • Decision Tree fundamentals (structure, splits, overfitting) • Entropy with formulas and worked examples • Gini Impurity and comparison with Entropy • Information Gain step-by-step calculation • Pruning techniques and common parameters • Decision Tree as Regressor • Variance Reduction with formula explanation • Ready-to-use Python code...
KNN & Naive Bayes Complete Handwritten Notes | Scaling, Distance Metrics, Confusion Matrix, Examples, GridSearchCV, Pipeline
These are clean, easy-to-understand handwritten notes covering KNN (k-Nearest Neighbors) and Naive Bayes, perfect for ML beginners, exam prep, and quick revision. Content taken from the PDF includes: Data Transformation & Scaling (Min-Max, Z-Score) — Page 1 StandardScaler workflow (fit, transform, avoiding leakage) — Page 1 Evaluation Metrics: Confusion Matrix, Accuracy, Precision, Recall, F1 Score — Page 2 Naive Bayes Theory: Bayes Theorem, Conditional Independence, P(y|x), f...
- Other
- • 6 pages •
These are clean, easy-to-understand handwritten notes covering KNN (k-Nearest Neighbors) and Naive Bayes, perfect for ML beginners, exam prep, and quick revision. Content taken from the PDF includes: Data Transformation & Scaling (Min-Max, Z-Score) — Page 1 StandardScaler workflow (fit, transform, avoiding leakage) — Page 1 Evaluation Metrics: Confusion Matrix, Accuracy, Precision, Recall, F1 Score — Page 2 Naive Bayes Theory: Bayes Theorem, Conditional Independence, P(y|x), f...
Feature Engineering + Logistic Regression Handwritten Notes (A4, Exam-Ready & Beginner-Friendly)
These handwritten A4 notes cover everything from Feature Engineering, Encoding, Scaling, Overfitting/Underfitting, all the way to Logistic Regression theory + formulas + code. Designed in a simple, easy-to-revise format with diagrams, graphs, and examples. Perfect for college exams, viva, ML assignments, interviews, and quick revision. Includes: One-hot encoding, dummy variable trap Derived & interaction features Scaling methods (Min-Max, Standardization, Robust Scaling) Overfit...
- Other
- • 9 pages •
These handwritten A4 notes cover everything from Feature Engineering, Encoding, Scaling, Overfitting/Underfitting, all the way to Logistic Regression theory + formulas + code. Designed in a simple, easy-to-revise format with diagrams, graphs, and examples. Perfect for college exams, viva, ML assignments, interviews, and quick revision. Includes: One-hot encoding, dummy variable trap Derived & interaction features Scaling methods (Min-Max, Standardization, Robust Scaling) Overfit...
Machine Learning Basics + Detailed Linear Regression Notes | Gradient Descent, MSE, OLS, Examples
These notes cover Machine Learning basics, supervised learning, regression fundamentals, Simple and Multiple Linear Regression, cost function, gradient descent, MSE, OLS, hypothesis function, training/testing workflow, model evaluation, and bias-variance concepts. Perfect for exams, interviews, assignments, and quick revision.
- Class notes
- • 7 pages •
These notes cover Machine Learning basics, supervised learning, regression fundamentals, Simple and Multiple Linear Regression, cost function, gradient descent, MSE, OLS, hypothesis function, training/testing workflow, model evaluation, and bias-variance concepts. Perfect for exams, interviews, assignments, and quick revision.