Introduction & Advantages over File System | Why DBMS is Needed? | DBMS | Lec. 01

Problem Solving on Principal Component Analysis (PCA) | Machine Learning From Zero | L.45

Principal Component Analysis (PCA) Explained from scratch | Machine Learning from Zero | L.44

Problem Solving Hierarchical Clustering | Agglomerative & Divisive Machine Learning From Zero |L.43

Hierarchical Clustering Explained: Agglomerative & Divisive | Machine Learning From Zero | L.42

K-Means Clustering Explained: Partition Based Clustering | Machine Learning from Zero | L.39

Problem Solving on K-Means & K-Medoids | Practice Questions | Machine Learning From Zero | L.41

K-Medoids Clustering Explained: Robust Partition Based Algorithm | Machine Learning from Zero | L.40

IIT Guwahati Machine Learning – GATE DA 2026 | Pattern, Expected Questions, Concepts & GATE PYQs

GATE DA 2026 IIT Guwahati Calculus & Optimization | Pattern, Questions, Concepts & GATE PYQs

GATE DA 2026 Linear Algebra: Pattern, PYQs & What IIT Guwahati Expects

GATE DA 2026 Probability & Statistics Pattern | What IIT Guwahati Expects

GATE DA 2026 | What IIT Guwahati Actually Tests | The Mistake 90% Aspirants Make

IIT Guwahati GATE CSE 2026: Engineering Mathematics Pattern & Expected Questions

IIT Guwahati Compiler Design – GATE CSE 2026 | Pattern, Expected Questions, Concepts & Problems

Unsupervised Learning Overview & Roadmap | Machine Learning from Zero | L.38

IIT Guwahati Algorithms – GATE CSE 2026 | Pattern, Expected Questions, Concepts & Sample Problems

IIT Guwahati C & Data Structures – GATE CSE 2026 | Pattern, Expected Questions, Concepts & Sample

IIT Guwahati Theory of Computation – GATE CSE 2026 | Pattern, Expected Questions, Concepts & Sample

IIT Guwahati Computer Networks – GATE CSE 2026 | Pattern, Expected Questions, Concepts & Sample

IIT Guwahati Database Management Systems –GATE CSE 2026 | Pattern, Expected Questions, Concepts

IIT Guwahati Operating Systems – GATE CSE 2026 | Pattern, Expected Questions, Concepts & Sample

IIT Guwahati GATE CSE 2026 Computer Organization & Architecture – | Pattern, Questions, Concepts

IIT Guwahati GATE CSE 2026 Digital Logic Design | Pattern, Expected Questions, Concepts & Sample

GATE CSE 2026 | Pattern | Expected Questions, Concepts & Sample Problems | All Subjects

Problem Solving – ReLU & Neural Networks Basics | Machine Learning from Zero | L.37

Problem Solving – Support Vector Machines & Kernel Trick| Machine Learning from Zero | L.35

Problem Solving – Decision Tree Gini & Entropy | Machine Learning from Zero | L.31

Problem Solving – Cross Validation & ROC AUC | Machine Learning from Zero | L.29

Problem Solving – Bayesian Linear Discriminant Analysis | Machine Learning from Zero | L.26

Problem Solving – Fisher Linear Discriminant Analysis (LDA) | Machine Learning from Zero | L.24

Problem Solving – k-NN Algorithm | Machine Learning from Zero | L.16

Problem Solving – Logistic Regression | Machine Learning from Zero | L.14

Problem Solving – Ridge Regression (L2) | Machine Learning from Zero | L.12

Problem Solving – Multiple Linear Regression | Machine Learning from Zero | L.10

Problem Solving – Linear Regression Standard Error | Machine Learning from Zero | L.07

Problem Solving – Confusion Matrix & Metrics | Machine Learning from Zero | L.04

Overview of Machine Learning: Types, Workflow & Data Explained | Machine Learning From Zero | L.01

Issues in ML: Bias, Underfitting & Overfitting Explained | Machine Learning From Zero | L.02

Confusion Matrix Explained: Accuracy, Precision, Recall & F1 Score | Machine Learning | L.03

Problem Solving – Naive Bayes Classifiers (GNB, MNB, BNB) | Machine Learning from Zero | L.22

Linear Regression Standard Error Explained with Examples | Machine Learning | L.06

Linear Regression Explained: OLS, RSS, R² & Error Metrics | Machine Learning From Zero | L.05

Neural Networks Explained: Neuron, Feedforward & Backpropagation Basics | Machine Learning | L.36

Evaluation & Cross-Validation Explained: Hold-Out, K-Fold & LOOCV | Machine Learning | L.27

Linear Discriminant Analysis (Bayesian Overview) Explained | Machine Learning From Zero | L.25

Bernoulli Naive Bayes Explained with Example | Machine Learning From Zero | L.21

Multinomial Naive Bayes Explained: Laplace Smoothing & Example | Machine Learning From Zero | L.20

Gaussian Naive Bayes Explained with Example | Machine Learning From Zero | L.19

Kernel Trick Explained: Types of Kernel Functions with Intuition | Machine Learning From Zero | L.34

Multiple Regression Explained: R² & Adjusted R² (Goodness of Fit)| Machine Learning From Zero | L.09

ROC Curve & AUC Explained: Bias–Variance Trade-off with Examples | Machine Learning From Zero | L.28

MAP Decision Rule (Maximum A Posteriori) Explained with Example | Machine Learning From Zero | L.18

SVM Soft Margin Explained: Geometry, Optimization & Example | Machine Learning From Zero | L.33

Primal SVM Optimization: From Constraints to Separating Hyperplanes L32

Ridge Regression Explained: L2 Regularization & OLS Issues | Machine Learning From Zero | L.11

Multiple Regression Explained: OLS, RSS & Step-by-Step Examples | Machine Learning From Zero | L.08

Naive Bayes Explained in 3 Simple Steps with Example | Machine Learning From Zero | L.17

Logistic Regression Explained: Odds, Logit & Classification | Machine Learning From Zero | L.13

Decision Tree Classifier Explained: Gini, Entropy & Example | Machine Learning From Zero | L.30