GATE 2026 Machine Learning Crash Course

Lesson 1: Machine Learning 01 | Introduction To Machine Learning | DA | GATE Crash Course

This introductory session covers the fundamentals of Machine Learning, including supervised and unsupervised learning. It explains key algorithms like regression, classification, SVM, and neural networks, and aligns the content with the GATE DA syllabus.

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Lesson 2: Machine Learning 02 | Linear Regression | DA | GATE Crash Course

This lesson dives into simple and multiple linear regression. It covers hypothesis functions, least squares fit, cost function optimization, and gradient descent, showing how to model continuous outputs.

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Lesson 3: Machine Learning 03 | Logistic Regression | DA | GATE Crash Course

This session focuses on logistic regression for binary classification. It explains the sigmoid function, probability estimation, and how to model binary outcomes, making it a key tool for classification tasks.

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Lesson 4: Machine Learning 04 | Logistic Regression & Decision Tree | DA | GATE Crash Course

This lesson covers the decision tree algorithm in detail, including entropy, information gain, and the Gini index. It explains how to build, prune, and interpret decision trees for classification.

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Lesson 5: Machine Learning 05 | Decision Tree & Random Forest | DA | GATE Crash Course

This session deepens the understanding of decision trees and introduces random forests. It revisits entropy, information gain, and Gini index, then explains how random forests combine multiple trees for better performance.

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Lesson 6: Machine Learning 06 | Support Vector Machine | DA | GATE Crash Course

This lesson focuses on Support Vector Machines (SVM). It explains the optimal separating hyperplane, margin maximization, and the role of support vectors in classification.

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Lesson 7: Machine Learning 07 | KNN & Naive Bayes | DA | GATE Crash Course

This session covers K-Nearest Neighbors (KNN) and Naive Bayes. KNN is introduced as an instance-based lazy learner, while Naive Bayes is explained as a probabilistic classifier based on Bayes' theorem.

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Lesson 8: Machine Learning 08 | Clustering Part 1 | DA | GATE Crash Course

This lesson introduces unsupervised learning with K-Means clustering. It walks through the iterative process of centroid selection, assignment, and updating until convergence.

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Lesson 9: Machine Learning 09 | Clustering Part 2 | DA | GATE Crash Course

This session moves beyond K-Means to hierarchical clustering. It covers the agglomerative (bottom-up) approach and how to interpret dendrograms, without needing to predefine the number of clusters.

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Lesson 10: Machine Learning 10 | Linear Discriminant Analysis | DA | GATE Crash Course

This lesson covers Linear Discriminant Analysis (LDA), a supervised dimensionality reduction technique. It explains how LDA finds the optimal linear separation between classes, contrasting with PCA.

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Lesson 11: Machine Learning 11 | Principal Component Analysis | DA | GATE Crash Course

This session focuses on Principal Component Analysis (PCA), an unsupervised dimensionality reduction technique. It explains how PCA transforms high-dimensional data into principal components that capture the most variance.

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Lesson 12: Machine Learning 12 | Neural Network | DA | GATE Crash Course

This lesson introduces neural networks, covering the basic architecture and concepts. It provides a foundation for understanding how neural networks learn and are applied in machine learning.

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