GATE 2026 Machine Learning Crash Course for Data Analytics

About this path

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.

What you will learn

• Understand core machine learning concepts and types (supervised, unsupervised).
• Apply linear and logistic regression for prediction and classification.
• Build and interpret decision trees using entropy, information gain, and Gini index.
• Explain ensemble methods like Random Forest and their advantages.
• Understand SVM, KNN, and Naive Bayes for classification tasks.
• Perform clustering using K-Means and hierarchical methods.
• Apply dimensionality reduction with LDA and PCA.
• Grasp the fundamentals of neural networks.

Who this is for

Self-paced learners who want a clear free path with creator-credited YouTube lessons.

Curriculum