Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Read by thought-leaders and decision-makers around the world. Phone Number: +1-650-246-9381 Email: pub@towardsai.net
228 Park Avenue South New York, NY 10003 United States
Website: https://towardsai.net/ Publisher: https://towardsai.net/#publisher Diversity Policy: https://towardsai.net/about Ethics Policy: https://towardsai.net/about Masthead: https://towardsai.net/about
Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Founders: Roberto Iriondo, Website, Job Title: Co-founder and Advisor Works for: Towards AI, Inc. Follow Roberto: X, LinkedIn, GitHub, Google Scholar, , Medium, ML@CMU, , Crunchbase, , Roberto Iriondo, Generative AI Lab, Generative AI LabVeloxTrendUltrarix Capital PartnersDenis Piffaretti, Job Title: Co-founder Works for: Towards AI, Inc.Louie Peters, Job Title: Co-founder Works for: Towards AI, Inc.Louis-François Bouchard, Job Title: Co-founder Works for: Towards AI, Inc. Cover:
Logo:
Areas Served: Worldwide Alternate Name: Towards AI, Inc. Alternate Name: Towards AI Co. Alternate Name: towards ai Alternate Name: towardsai Alternate Name: towards.ai Alternate Name: tai Alternate Name: toward ai Alternate Name: toward.ai Alternate Name: Towards AI, Inc. Alternate Name: towardsai.net Alternate Name: pub.towardsai.net
Follow us on: FacebookXLinkedInInstagramYoutubeGithubGoogle My BusinessGoogle SearchGoogle NewsGoogle MapsDiscordShopTowards AI, Medium EditorialMediumFlipboardPublicationFeedSponsorsSponsorsContribute
5 stars – based on 497 reviews
Frequently Used, Contextual References
TODO: Remember to copy unique IDs whenever it needs used. i.e., URL: 304b2e42315e
Resources
Free: 6-day Agentic AI Engineering Email Guide.
_Learnings from Towards AI's hands-on work with real clients._
Crack ML Interviews with Confidence: K-Nearest Neighbors (KNN 20 Q&A)
Last Updated on April 29, 2026 by
Author(s): Shahidullah Kawsar
Originally published on Towards AI.
Data Scientist & Machine Learning Interview Preparation
How to train a ML model using KNN in 5 steps:
Source: This image is generated by ChatGPT
The article provides a comprehensive overview of K-Nearest Neighbors (KNN), a popular machine learning algorithm, detailing its fundamental concepts such as similarity-based learning, distance calculations, prediction rules, and the importance of selecting an appropriate value for K. It explores key considerations like feature scaling, the implications of the lazy learning approach, and practical applications, reinforced by a series of interview questions and answers that assess knowledge of KNN, its advantages, and its challenges in high-dimensional spaces.
Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
_Note: Article content contains the views of the contributing authors and not Towards AI._
Related posts
The Event-Driven Blueprint: How I Scaled a Spring Boot System to 10 Million Kafka Messages/Day
3 likes Artificial Intelligence, Data Science, Latest, Machine Learning
Building Vector Search? Why FAISS Alone Isn’t Enough
3 likes Artificial Intelligence, Latest, Machine Learning
TAI #202: GPT-5.5 Moves Codex Into Real Work
Artificial Intelligence, Latest, Machine Learning
Machine Learning System Design -The Model Serving Triangle, With One Forward Pass Flowing Through Every Trade-off (Part3)
Recent Posts
Crack ML Interviews with Confidence: K-Nearest Neighbors (KNN 20 Q&A)")
April 29, 2026 ##### The Event-Driven Blueprint: How I Scaled a Spring Boot System to 10 Million Kafka Messages/Day
Machine Learning System Design -The Model Serving Triangle, With One Forward Pass Flowing Through Every Trade-off (Part3)")
April 28, 2026 ##### AI Orchestration in Action: How MuleSoft and LLMs Fuel the Future of Enterprise AI
April 23, 2026 ##### GPT-4 Has 1.8 Trillion Parameters. It Uses 2% of Them Per Token.
April 22, 2026 ##### Part 20: Data Manipulation in Multi-Dimensional Aggregation
AIAlgorithmsArtificial IntelligenceBig DataBusinessChatgptClassificationComputer Sciencecomputer visionDataData AnalysisData ScienceData VisualizationDeep LearningeducationFinanceGenerative AiImage ProcessingInnovationLarge Language ModelsLinear RegressionLlmmachine learningMathematicsMlopsNaturallanguageprocessingNeural NetworksNLPOpenAIPandasProgrammingPythonresearchscienceSoftware DevelopmentStartupStatisticstechnologyTensorflowThesequenceTowards AITowards AI - MediumTowards AI — Multidisciplinary Science Journal - MediumTransformers
GDPR CCPA Statement
In order for Towards AI to work properly, we log user data. By using Towards AI, you agree to our Privacy Policy, including our cookie policy.
