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所在平台: Udemy |
课程主页: https://www.udemy.com/course/750-ai-data-science-interview-questions-2025-edition/
课程评论：没有评论
**课程名称：** 750+ AI 与数据科学面试题 - 2025 版 **课程概述：** 本课程是一份全方位备考指南，专为准备数据科学、机器学习和人工智能职位的学员设计。无论您目标是高增长的初创公司还是全球科技巨头，本课程都提供有针对性、场景驱动的准备，以增强您的信心和表现。 **课程内容亮点：** * **真实数据科学/机器学习/人工智能面试题：** 涵盖顶级科技公司常考问题，包含理论及实际应用题，并附带深入的追问。 * **场景式面试题：** 侧重于商业和生产环境中的问题解决，包括模型故障、数据管道问题以及决策权衡等挑战，同样附带深入的追问。 * **项目相关面试题（简历友好）：** 涵盖与项目领域无关的常见问题，帮助您清晰解释、论证并延伸您简历中的任何项目。 * **Python 面试题：** 聚焦于数据科学核心及应用 Python，包括列表/字典操作、NumPy、Pandas、OOP、错误处理、FastAPI、设计模式和 SOLID 原则。 * **SQL 面试题：** 侧重于 SQL 基础、复杂查询、优化及数据操作。 * **架构与系统设计题：** 帮助您理解和解释端到端的机器学习流程和实际架构，涵盖模型版本控制、部署、CI/CD、可扩展性、监控等。 * **云无关的机器学习与人工智能题：** 为在 AWS、Azure 和 GCP 上部署和扩展模型提供面试准备。题目侧重于跨所有云平台的通用概念。
This all-in-one guide is crafted for aspirants preparing for Data Science, Machine Learning, and AI roles. Whether you're targeting high-growth startups or global tech giants, this course offers focused, scenario-driven prep to boost your confidence and performance.Real-World Data Science / ML / AI Interview QuestionsCovers frequently asked questions from top tech companiesIncludes both theory and practical application-oriented questionsIncluding insightful follow-up questions.Scenario-Based Interview QuestionsProblem-solving in business and production contextsChallenges based on model failures, data pipeline issues, and decision trade-offsIncluding insightful follow-up questionsProject-Specific Interview Questions (Resume-Friendly)Common questions asked regardless of the domain of your projectHelps you explain, defend, and extend any project you've mentionedPython Interview QuestionsFocused on core and applied Python for data science.Includes topics like list/dict operations, NumPy, Pandas, OOP, error handling, FastAPI, Design Patterns, and SOLID Principles.SQL Interview QuestionsFocused on SQL fundamentals, complex queries, optimization, and data manipulation.Architecture & System Design QuestionsUnderstand and explain end-to-end ML pipelines and real-world architecturesCovers model versioning, deployment, CI/CD, scalability, monitoring, etc.Cloud-Agnostic ML & AI QuestionsInterview prep for deploying and scaling models across AWS, Azure, and GCPQuestions focus on concepts that are common across all cloud platforms
