Machine Learning
A free, structured learning path covering core machine learning topics for the GATE DA exam, from basics to neural networks.
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Now playing: Machine Learning 01 | Introduction To Machine Learning | DA | GATE Crash Course
This learning path is built from a GATE 2026 crash course playlist by Parth Sir, tailored for the Data Analytics (DA) stream. It walks through essential machine learning concepts in a logical order: starting with an introduction, then covering regression, classification, decision trees, ensemble methods, SVM, KNN, Naive Bayes, clustering, dimensionality reduction, and neural networks. Each lesson includes exam-focused explanations and practical examples to help you build a strong foundation for the GATE DA machine learning section.
Self-paced learners who want a clear free path with creator-credited YouTube lessons.
Lesson 1: Machine Learning 01 | Introduction To Machine Learning | DA | GATE Crash Course
This lesson introduces machine learning for GATE DA, covering supervised and unsupervised learning, and key algorithms like regression, classification, SVM, and neural networks. It sets the foundation for the crash course with exam-focused strategies.
Open on YouTubeLesson 2: Machine Learning 02 | Linear Regression | DA | GATE Crash Course
This session covers simple and multiple linear regression, including hypothesis functions, least squares fit, cost function optimization, and gradient descent. It explains how to model continuous outputs from input features.
Open on YouTubeLesson 3: Machine Learning 03 | Logistic Regression | DA | GATE Crash Course
This lesson focuses on logistic regression for binary classification. It explains the sigmoid function, probability estimation, and how to model binary outcomes, which is essential for classification tasks in GATE DA.
Open on YouTubeLesson 4: Machine Learning 04 | Logistic Regression & Decision Tree | DA | GATE Crash Course
This video explains decision trees in detail, covering entropy, information gain, and Gini index for optimal splits. It also discusses building, pruning, and interpreting decision trees, along with logistic regression concepts.
Open on YouTubeLesson 5: Machine Learning 05 | Decision Tree & Random Forest | DA | GATE Crash Course
This session dives deeper into decision trees and introduces Random Forest. It covers entropy, information gain, and Gini index for split selection, and explains how ensemble methods improve performance.
Open on YouTubeLesson 6: Machine Learning 06 | Support Vector Machine | DA | GATE Crash Course
This lesson covers Support Vector Machines (SVM), focusing on the optimal separating hyperplane and margin maximization. It explains the role of support vectors and the geometric intuition behind maximum margin classification.
Open on YouTubeLesson 7: Machine Learning 07 | KNN & Naive Bayes | DA | GATE Crash Course
This video explains K-Nearest Neighbors (KNN) and Naive Bayes. KNN is an instance-based lazy learner, while Naive Bayes is a probabilistic classifier. The lesson breaks down their working principles and contrasts them.
Open on YouTubeLesson 8: Machine Learning 08 | Clustering Part 1 | DA | GATE Crash Course
This lesson introduces unsupervised learning with K-Means clustering. It covers the iterative process of centroid selection, assignment, and updating, and discusses how to choose the number of clusters.
Open on YouTubeLesson 9: Machine Learning 09 | Clustering Part 2 | DA | GATE Crash Course
This session moves beyond K-Means to hierarchical clustering, including the agglomerative (bottom-up) approach and how to interpret dendrograms. It covers algorithms that don't require pre-defining cluster numbers.
Open on YouTubeLesson 10: Machine Learning 10 | Linear Discriminant Analysis | DA | GATE Crash Course
This lesson covers Linear Discriminant Analysis (LDA), a supervised dimensionality reduction technique. It focuses on finding the optimal linear separation between classes, distinct from PCA.
Open on YouTubeLesson 11: Machine Learning 11 | Principal Component Analysis | DA | GATE Crash Course
This video explains Principal Component Analysis (PCA) for dimensionality reduction. It covers transforming high-dimensional data into principal components that capture the most variance, simplifying data for analysis.
Open on YouTubeLesson 12: Machine Learning 12 | Neural Network | DA | GATE Crash Course
This lesson introduces neural networks, covering basic architecture, activation functions, and how they learn. It provides a foundation for understanding deep learning concepts relevant to GATE DA.
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