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Introduction to Applied Machine Learning · LearnSpace
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Introduction to Applied Machine Learning

Курс от Alberta Machine Intelligence Institute
Средний≈ 6.8 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project. By the end of the course, you will be able to clearly define a machine learning problem using two approaches. You will learn to survey available data resources and identify potential ML applications. You will learn to take a business need and turn it into a machine learning application. You will prepare data for effective machine learning applications. This is the first course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.

Навыки, которые вы освоите

Machine LearningData CollectionSupervised LearningData EthicsApplied Machine LearningUnsupervised LearningMachine Learning AlgorithmsData QualityCase StudiesBusiness PrioritiesBusiness AnalysisData ProcessingData PreprocessingProduct Lifecycle ManagementAI EnablementMachine Learning MethodsBusiness Requirements

Программа курса

4 модулей · 64 учебных материалов

01Introduction to Machine Learning Applications23 материалов

Lesson 1: Definitions

Introduction to the Applied Machine Learning SpecializationВидеоInstructor IntroductionВидеоIntroduction to Course 1ВидеоWhat is Artificial Intelligence and Machine Learning?ВидеоWhat about Data Science?

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Anna Koop

Senior Scientific Advisor

Introduction to Applied Machine Learning
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Обучение на Coursera

≈ 6.8 ч

4 модулей

Язык: Английский

Субтитры: Арабский, Французский, Бенгальский, Узбекский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Урду, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Дари, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
Видео
The Machine Learning ProcessВидео
Artificial Intelligence ViewpointsОбсуждение
What about Deep Learning? (supplemental)Чтение
Fooling Neural Networks (supplemental)Чтение
Concepts and DefinitionsЗадание
Meet and Greet!Обсуждение
Misunderstandings surrounding AI and MLОбсуждение

Lesson 2: Supervised Learning

The Three Kinds of Machine LearningВидеоClassification: What is it and how does it work?ВидеоRegression: Fitting lines and predicting numbersВидеоHow to Curate A Ground Truth For Your Business Dataset (Required)ЧтениеLearning From Multiple Annotators: A Survey (supplemental)ЧтениеInferring the Ground Truth Through Crowdsourcing (supplemental)Чтение

Lesson 3: Broader Machine Learning

Unsupervised LearningВидеоSemi Supervised Learning (required)ЧтениеReinforcement LearningВидеоWeekly SummaryВидеоIdentifying Machine Learning TechniquesЗадание
02Machine Learning in the Real World15 материалов

Lesson 1: Machines are Different From Humans

Generalization and how machines actually learnВидеоFeatures and transformations of raw dataВидеоExplainability and Accuracy for a QuAMОбсуждение

Lesson 2: Applied Scenarios

A Brief Introduction into Precision AgricultureЧтениеFarmer Betty and Her Precision Agriculture PlansВидеоWhat to consider when using your QuAMВидеоFarmer Betty Tried Unsupervised Learning (required)ЧтениеData is Central to Your ML Problem (required)Чтение

Lesson 3: Getting Good Questions

Broad Examples Narrowed DownВидеоIdentify Business EvaluationВидеоEverything is a ProxyВидеоAll About ProxiesОбсуждениеMartin Zinkevich's Rules for ML (supplemental)ЧтениеWeekly SummaryВидео
03Learning Data14 материалов

Lesson 1: Data Needs

Sources of Training DataВидеоHow Much Data Do I Need?ВидеоSources of DataОбсуждение

Lesson 2: Data Relates to Problems

Ethical IssuesВидеоData Protection Laws (required)ЧтениеGovernment readings on data privacy (supplemental)ЧтениеBias in Data SourcesВидеоNoise and Sources of RandomnessВидеоBias and NoiseОбсуждение

Lesson 3: Data Process

Image Classification ExampleВидеоData Cleaning: Everybody's favourite taskВидеоWhy you need to set up a Data PipelineВидеоWeekly SummaryВидеоUnderstanding Data for MLЗадание
04Machine Learning Projects 12 материалов

Lesson 1: Machine Learning Process Lifecycle

MLPL OverviewВидеоMachine Learning Process Lifecycle ExplainedЧтениеMLPL as experienced by Farmer BettyВидео

Lesson 2: Getting Ready to Model

Exploring the process of problem definitionВидеоAssessing your QuAM for use in your BusinessВидеоWhat task can machine learning help you with?Обсуждение

Lesson 3: Model Learning and Evaluation

Technically Assessing the Strength of your QuAMВидеоDifferent Kinds of WrongВидеоFalse Positives and False NegativesОбсуждениеWeekly SummaryВидеоDeep Learning for Identifying Metastatic Breast Cancer (advanced supplemental)ЧтениеUnderstanding Machine Learning ProjectsЗадание
Machine Learning in the Real World ReviewЗадание