Course Details

Course Overview

This course is a complete preparation program for the Artificial Intelligence and Machine Learning section of GATE Data Science & Artificial Intelligence (DA) 2026/2027. Designed according to the latest GATE syllabus, it covers supervised learning, unsupervised learning, search algorithms, logic, reasoning under uncertainty, Bayesian inference, and neural networks with an exam-oriented approach. Students will master Linear Regression, Logistic Regression, Decision Trees, SVM, KNN, Naïve Bayes, LDA, PCA, clustering algorithms, search strategies, propositional and predicate logic, Bayesian Networks, and inference techniques. Every topic is explained from fundamentals to GATE-level problem solving using conceptual explanations, numerical examples, previous year questions, quizzes, assignments, and mock tests. The course focuses on improving conceptual understanding, analytical thinking, and exam-solving speed, helping students confidently tackle AI and ML questions in GATE DA.

This course is designed for guided live learning. You will move through concepts, Tutor-led demonstrations, practice tasks, review sessions, assignments and a final project that proves your understanding.

Learning Outcomes

• Master the complete GATE DA AI & Machine Learning syllabus
• Understand supervised and unsupervised learning techniques
• Solve regression and classification problems
• Implement Linear and Logistic Regression
• Learn Ridge Regression and Bias-Variance Trade-off
• Master KNN, Naïve Bayes, LDA, SVM, and Decision Trees
• Understand Neural Networks and Multi-Layer Perceptrons
• Perform Clustering using K-Means, K-Medoids, and Hierarchical Clustering
• Apply Principal Component Analysis (PCA)
• Understand informed, uninformed, and adversarial search
• Learn Propositional and Predicate Logic
• Master reasoning under uncertainty
• Understand Bayesian Networks and Conditional Independence
• Perform Exact and Approximate Inference
• Solve GATE previous year questions with confidence
• Develop exam-oriented problem-solving techniques

Requirements

• Basic Mathematics
• Basic Probability and Statistics
• Basic Python knowledge (helpful but not mandatory)
• Engineering or Science background
• Laptop or Desktop
• Commitment to regular practice

Who This Course Is For

• GATE DA 2026 Aspirants
• GATE DA 2027 Aspirants
• B.Tech Students
• B.Sc. Students
• MCA Students
• M.Sc. Students
• Computer Science Students
• Data Science Students
• Artificial Intelligence Enthusiasts
• Working Professionals preparing for GATE

Assignments

Assignments help the Tutor check your progress and give completion feedback. After purchase, assigned work also appears inside the student dashboard.

Tutor Details

Syed Nasrullah

Data Science Tutor

Live Tutor focused on practical Data Science course outcomes, guided projects and clear student feedback.

Certified Data Science practitioner 4 years live teaching Weekday evenings

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Message to Tutor - Syed Nasrullah