GateXAIML
100% Free GATE DA

DA - Machine Learning - Supervised Learning

7 Chapters 50 Video Lectures Notes + PYQs PDF
Your Progress 0 / 50 videos watched (0%)
Home View Book Download
Ch. 1 Ch. 2 Ch. 3 Ch. 4 Ch. 5 Ch. 6 Ch. 7
L1 - Overview of Machine Learning: Types, Workflow & Data Explained
L2 - Issues in ML: Bias, Underfitting & Overfitting Explained
L3 - Confusion Matrix Explained: Accuracy, Precision, Recall & F1 Score
L4 - Problem Solving: Confusion Matrix (Problems 1–11)
L5 - Linear Regression Explained: OLS, RSS, R² & Error Metrics
L6 - Linear Regression Standard Error Explained with Examples
L7 - Problem Solving: Linear Regression Standard Error (Problems 12–23)
L8 - Multiple Regression Explained: OLS, RSS & Step-by-Step Examples
L9 - Multiple Regression Explained: R² & Adjusted R² (Goodness of Fit)
L10 - Problem Solving: Multiple Linear Regression (Problems 24–41)
L11 - Ridge Regression Explained: L2 Regularization & OLS Issues
L12 - Problem Solving: Ridge Regression (Problems 42–64)
L13 - Logistic Regression Explained: Odds, Logit & Classification
L14 - Problem Solving: Logistic Regression (Problems 65–79)
L15 - k-Nearest Neighbors (k-NN): Concepts, Intuition & Examples
L16 - Problem Solving: k-NN Algorithm (Problems 80–92)
L17 - Naive Bayes Explained in 3 Simple Steps with Example
L18 - MAP Decision Rule (Maximum A Posteriori) Explained with Example
L19 - Gaussian Naive Bayes Explained with Example
L20 - Multinomial Naive Bayes Explained: Laplace Smoothing & Example
L21 - Bernoulli Naive Bayes Explained with Example
L22 - Problem Solving: Naive Bayes Classifiers (Problems 93–113)
L27 - Evaluation & Cross-Validation Explained: Hold-Out, K-Fold & LOOCV
L28 - ROC Curve & AUC Explained: Bias–Variance Trade-off with Examples
L29 - Problem Solving: Cross Validation & ROC AUC (Problems 144–174)
L23 - Linear Discriminant Analysis Explained with Example
L24 - Problem Solving: Fisher Linear Discriminant Analysis (Problems 114–133)
L25 - Linear Discriminant Analysis (Bayesian Overview) Explained
L26 - Problem Solving: Bayesian Linear Discriminant Analysis (Problems 134–143)
L30 - Decision Tree Classifier Explained: Gini, Entropy & Example
L31 - Problem Solving: Decision Tree Gini & Entropy (Problems 175–196)
L32 - SVM Hard Margin Explained: Geometry, Optimization & Example
L33 - SVM Soft Margin Explained: Geometry, Optimization & Example
L34 - Kernel Trick Explained: Types of Kernel Functions with Intuition
L35 - Problem Solving: Support Vector Machines & Kernel Trick (Problems 197–225)
L36 - Neural Networks Explained: Neuron, Feedforward & Backpropagation Basics
L37 - Problem Solving: ReLU & Neural Networks Basics (Problems 226–250)
Solutions to Problems 1–11 (Lecture 4)
Solutions to Problems 12–23 (Lecture 7)
Solutions to Problems 24–41 (Lecture 10)
Solutions to Problems 42–64 (Lecture 12)
Solutions to Problems 65–79 (Lecture 14)
Solutions to Problems 80–92 (Lecture 16)
Solutions to Problems 93–113 (Lecture 22)
Solutions to Problems 114–133 (Lecture 24)
Solutions to Problems 134–143 (Lecture 26)
Solutions to Problems 144–174 (Lecture 29)
Solutions to Problems 175–196 (Lecture 31)
Solutions to Problems 197–225 (Lecture 35)
Solutions to Problems 226–250 (Lecture 37)