ML & Data Science
23 articles in this topic
AI Agent Architecture and Program Types MCQs: 11 Solved Questions
Eleven solved AI agent MCQs with the trap in each one named: current-percept rules, sensor versus actuator, goal versus utility scoring, and what counts as learning.
Knowledge Representation in Artificial Intelligence: Logic, Semantic Networks, Frames and Rules with Worked Examples
Learn how AI stores facts and derives new ones through logic, semantic networks, frames and rules. Two worked traces make the inference steps exam-ready.
Search Algorithms in AI: BFS, DFS, UCS and A* Explained on One Worked Graph
Run five major AI search algorithms on the same weighted graph, compare the paths they return, and check admissibility and consistency with real numbers.
Natural Language Processing in AI: Core Concepts, a Worked Bigram Example, and How Exams Test It
Build NLP from tokens to transformers, then reproduce the TF-IDF and bigram calculations that exam and placement questions expect.
Multi Agent Systems in AI: Concepts, Nash Equilibrium and Auctions with Worked Examples
Learn the complete MAS concept set, then compute a Nash equilibrium, a minimax choice and a Vickrey auction outcome step by step.
Artificial Neural Networks: Complete Guide with Worked Examples for GATE and Interviews
Turn ANN diagrams into numbers. Work through a neuron, activation derivatives, OR and XOR, one gradient update, backpropagation and parameter counts.
Genetic Algorithms in AI: Complete Guide with a Worked One-Generation Example
Learn the complete genetic algorithm loop, then calculate selection, crossover, mutation, and fitness improvement by hand on a four-chromosome population.
Game Playing in AI: Minimax, Alpha-Beta Pruning, and How Exams Test Them
Build the full game-playing AI toolkit through one tree solved first with minimax and then alpha-beta pruning, plus evaluation functions, chance nodes, and exam traps.
Fuzzy Sets in Artificial Intelligence: Membership Functions, Operations, and Worked Examples
Learn fuzzy sets through one age-based example, from membership grades and alpha-cuts to max-min relation composition. Includes exact exam traps and revision rules.
Artificial Intelligence for GATE: Syllabus Areas, Weightage Pattern and Prep Order
AI is tested through the GATE DA paper, not as a section of GATE CS. See what to study, how recent papers have distributed marks, and which order avoids rework.
Approaches to AI: The Four Approaches, a Worked Rational-Agent Example, and How Exams Test Them
Separate the four classic approaches to AI, score a rational vacuum agent step by step, and learn the distinctions that matter in exams and interviews.