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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-interview-questions-and-answers/
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课程名称：机器学习面试问题与答案 概述： 欢迎参加Uplatz提供的“机器学习面试问题与答案”课程。本课程专注于机器学习工程师和数据科学家职位面试中最常见的问题，帮助学习者熟悉与机器学习相关的热门问题及其详尽的答案。根据Indeed的数据，机器学习工程师在美国的平均年薪为149,750美元，其他国家也有类似的高薪待遇。随着越来越多的组织将机器学习作为创新和推动增长的关键支柱，该领域对于聪明和充满热情的机器学习工程师有着广阔的前景，不仅提供优厚的薪资，还面临多样的挑战。 什么是机器学习？ 机器学习是人工智能的一个快速发展的子领域，系统通过数据、统计和试错学习，以优化过程和加速创新。机器学习使计算机具备类似人类的学习能力，能够解决一些世界上最棘手的问题，从癌症研究到气候变化。机器学习使得系统能够从实例和经验中学习，而无需显式编程。用户只需向通用算法输入数据，算法将根据给定的数据创建逻辑，从而使计算机无需编程即可工作。机器学习工程师的职责包括创建程序和算法，使机器能够自主采取行动，例如开发自动驾驶汽车或个性化新闻推送。 机器学习工程师的角色包括： - 设计和构建可扩展的、可靠的数据管道，以实时处理大规模数据。 - 应用计算机科学基础知识，包括数据结构、算法和计算复杂性。 - 使用出色的数学技能，执行计算和处理相关算法。 - 产生项目成果并识别需要解决的问题，提高程序的有效性。 - 与数据工程师合作，构建数据和模型管道。 - 选择适合执行和产品化机器学习管道的平台。 - 开发符合业务需求的机器学习解决方案，并推动新技术的应用。 - 支持行业级高质量生产系统的实现，管理所需基础设施和数据管道。 该课程将帮助您深入了解机器学习的核心概念和实际应用，为即将到来的面试做好充足的准备。通过系统的学习和精炼的内容，您将能够掌握并应用机器学习的相关知识。
A warm welcome to the Machine Learning Interview Questions & Answers course by Uplatz.Uplatz provides this course on the most frequently asked questions in Machine Learning Engineer / Data Scientist job interviews. In this Machine Learning interview questions course, you will learn and get familiarized with the correct and comprehensive answers to the trending questions related to Machine Learning.According to Indeed, the average salary for a machine learning engineer is $149,750 per year in the United States and similar high salaries in other countries too. With more and more organizations making machine learning as a key pillar for innovation and driving growth, there is huge scope for smart and enthusiastic Machine Learning engineers. It is one of the fields having great career prospects, both in terms of the compensation offered as well as considering the variety of challenges available.What is Machine Learning?Machine learning is the fastest growing subfield of artificial intelligence, where systems have the ability to "learn" through data, statistics and trial and error in order to optimize processes and innovate at quicker rates. Machine learning is giving computers the ability to develop human-like learning capabilities that are allowing them to solve some of the world's toughest problems, ranging from cancer research to climate change.Machine Learning facilitates a system to learn from examples and experience without being explicitly programmed. Hence instead of writing code, what you do is you feed data to the generic algorithm, and the algorithm/ machine itself builds the logic based on the given data. Thus, Machine learning is the science of enabling computers to function without being programmed to do so. By combining software engineering and data analysis, machine learning engineers enable machines to learn without the need for further programming. As a machine learning engineer, working in this branch of artificial intelligence, you'll be responsible for creating programs and algorithms that enable machines to take actions without being directed. An example of a system you may produce is a self-driving car or a customized newsfeed.This branch of artificial intelligence can enable systems to identify patterns in data, make decisions, and predict future outcomes. Machine Learning can help companies determine the products you're most likely to buy and even the online content you're most likely to consume and enjoy. Machine Learning makes it easier to analyze and interpret massive amounts of data, which would otherwise take decades or even an eternity for humans to decode.Roles of a Machine Learning engineerDesign and Build distributed, scalable, and reliable data pipelines that ingest and process data at scale and in real-timeApply computer science fundamentals, including data structures, algorithms, computability and complexity and computer architectureUse exceptional mathematical skills, in order to perform computations and work with the algorithms involved in this type of programmingProduce project outcomes and isolate the issues that need to be resolved, in order to make programs more effectiveCollaborate with data engineers to build data and model pipelinesSelect appropriate platforms for the execution and productization of ML pipelinesDevelop and productionize machine learning solutions aligned to business needs and push the boundaries by suggesting and driving new technologiesSupport the implementation of industry-scale high-quality production systemsManage the infrastructure and data pipelines needed to bring code to productionUnderstand how to combine data architectures, distributed systems, machine learning, and next-generation user interfacesBuild algorithms based on statistical modelling procedures and build and maintain scalable machine learning solutions in productionUse data modelling and evaluation strategy to find patterns and predict unseen instancesApply machine learning algorithms and librariesCreate advanced analytics and machine learning driven solutions for varying data volumes, data types and formatsDesign machine learning systems, and oversee the platform on which the solutions would be deployedAnalyze large, complex datasets to extract insights and decide on the appropriate techniqueResearch and implement best practices to improve the existing machine learning infrastructureProvide support to engineers and product managers in implementing machine learning in the product
