К содержимому
learnspaceYOUR NEXT CHAPTER
ПРОСТРАНСТВО ОБУЧЕНИЯ
ГлавнаяКаталог курсовМоё обучениеCoursera

Знания без границ

Учитесь у лучших университетов и компаний мира.

Открыть Coursera
Интеграция
Пространство университета
Моё пространствоСтраница курса
↵
ЯЛичный кабинетСтудент
© 2026 LearnSpaceКаждый день — возможность узнать больше.Помощь
Managing Machine Learning Projects with Google Cloud · LearnSpace
Назад в каталог
courseraАнализ данных

Managing Machine Learning Projects with Google Cloud

Курс от Google Cloud
Начальный≈ 13.5 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Business professionals in non-technical roles have a unique opportunity to lead or influence machine learning projects. If you have questions about machine learning and want to understand how to use it, without the technical jargon, this course is for you. Learn how to translate business problems into machine learning use cases and vet them for feasibility and impact. Find out how you can discover unexpected use cases, recognize the phases of an ML project and considerations within each, and gain confidence to propose a custom ML use case to your team or leadership or translate the requirements to a technical team.

Навыки, которые вы освоите

Google Cloud PlatformMachine LearningApplied Machine LearningModel EvaluationResponsible AIModel TrainingData StrategyFeasibility StudiesBusiness AnalysisMachine Learning SoftwareData GovernanceModel DeploymentData EthicsImage AnalysisMachine Learning MethodsInnovation

Программа курса

8 модулей · 55 учебных материалов

01Module 1: Introduction3 материалов

Module 1: Welcome to the course

IntroductionВидеоHow to download course resourcesЧтениеHow to send feedbackЧтение
02Module 2: Identifying business value for using ML6 материалов

Module 2: Introduction

Учитесь у экспертов

Google Cloud Training

Преподаватель курса

Managing Machine Learning Projects with Google Cloud
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 13.5 ч

8 модулей

Язык: Английский

Субтитры: Арабский, Французский, Китайский (Китай), Итальянский, Бразильский португальский, Корейский, Немецкий, Индонезийский, Испанский, Японский, Малайский

Часть программы вашего университета
IntroductionВидео

Understanding ML with examples

AI vs ML vs Deep LearningВидео

Module 2: Machine learning projects

Phase 1: Assess feasibilityВидеоPractice assessing the feasibility of ML use casesВидео

Assignments

Identifying business value for using MLЗаданиеWorksheetЧтение
03Module 3: Defining ML as a practice11 материалов

Module 3: Introduction

Common ML problem typesВидео

Machine Learning Defined

Standard algorithm and dataВидеоData qualityВидеоPredictive insights and decisionsВидео

Real-life Use Cases

More ML examplesВидеоPractice series: Analyze the ML use caseВидеоModule 3: WorksheetЧтениеSaving the world's beesВидеоGoogle Assistant for accessibilityВидеоExercise review and Why ML nowВидео

Assessment

Defining ML as a practiceЗадание
04Module 4: Building and evaluating ML models8 материалов

Module 4: Introduction

Features and labelsВидео

Tools to label datasets

Building labeled datasetsВидео

Guidelines for training ML models

Training an ML modelВидеоGeneral best practicesВидео

Getting started with Qwiklabs

Introduction to hands-on labsВидеоIdentifying damaged car parts with AutoML VisionВнешний инструментLab 1: ReviewВидео

Assessment

Building and evaluating ML modelsЗадание
05Module 5: Using ML responsibly and ethically8 материалов

Module 5: Introduction

Human bias in MLВидео

Guiding Principles

Google's AI PrinciplesВидеоCommon types of human biasВидео

ML fairness

Evaluating model fairnessВидео

Hands-on Lab 2

Guidelines and Hands-on LabВидеоInspecting a dataset for bias using TensorFlow Data Validation and FacetsВнешний инструментLab 2: ReviewВидео

Assessment

Using ML responsibly and ethicallyЗадание
06Module 6: Discovering ML use cases in day-to-day business9 материалов

Module 6: Introduction

Replacing rule-based systems with MLВидеоAutomate processes and understand unstructured dataВидео

Beyond the basics

Personalize applications with MLВидеоCreative uses of MLВидео

Worksheet and Hands-on Lab 3

Sentiment analysis and Hands-on LabВидеоSentiment Analysis WorksheetЧтениеSentiment Analysis with Natural Language APIВнешний инструментLab 3: ReviewВидео

Assessment

Discovering ML use cases in day-to-day businessЗадание
07Module 7: Managing ML projects successfully9 материалов

Module 7: Introduction

Key consideration 1: business valueВидео

Key consideration 2

Data strategy (pillars 1–3)ВидеоData strategy (pillars 4–7)Видео

Key consideration 3 and 4

Data governanceВидеоBuild successful ML teamsВидео

Key consideration 5 and Hands-on Lab

Create a culture of innovation and Hands-on LabВидеоEvaluate an ML model with BigQuery MLВнешний инструментLab 4: ReviewВидео

Assessment

Managing ML projects successfullyЗадание
08Module 8: Summary1 материалов

Module 8: Course Summary

SummaryВидео