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Unsupervised Machine Learning · LearnSpace
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Unsupervised Machine Learning

Курс от IBM
Средний≈ 22.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning. By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

Навыки, которые вы освоите

Unsupervised LearningDimensionality ReductionMachine Learning AlgorithmsText MiningMachine LearningBig DataApplied Machine LearningData PreprocessingScikit Learn (Machine Learning Library)Model EvaluationPerformance Metric

Программа курса

7 модулей · 82 учебных материалов

01Introduction to Unsupervised Learning and K Means19 материалов

Introduction to Unsupervised Learning

Course IntroductionВидеоIntroduction to Unsupervised Learning: OverviewВидеоIntroduction to Unsupervised Learning: Use Cases of ClusteringВидеоIntroduction to ClusteringВидео

Учитесь у экспертов

Mark J Grover

Digital Content Delivery Lead

Miguel Maldonado

Machine Learning Curriculum Developer

Joseph Santarcangelo

Ph.D., Data Scientist at IBM

Xintong Li

Data Scientist at IBM

Unsupervised Machine Learning
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Обучение на Coursera

≈ 22.7 ч

7 модулей

Язык: Английский

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

Часть программы вашего университета
Ungraded: Introduction to Unsupervised LearningЗадание

K Means Clustering

K-Means ВидеоK-Means Initialization ВидеоSelecting the Right Number of Clusters in K-Means ВидеоElbow method and Applying K-meansВидеоK Means Demo (Activity)Внешний инструмент(Optional) K Means Notebook - Part 1ВидеоK Means Notebook - Part 2Видео(Optional) K Means Notebook - Part 3ВидеоPractice Lab: K Means Clustering LabВнешний инструментMixture of Gaussians ЧтениеPractice Lab: Mixture of Gaussians LabВнешний инструментUngraded: K Means ClusteringЗадание

Module Summary & Assessment

SummaryЧтениеGraded: Module 1 QuizЗадание
02Distance Metrics & Computational Hurdles11 материалов

Computational hurdles of clustering algorithms

Distance Metrics: Euclidean and Manhattan DistanceВидеоDistance Metrics: Cosine and Jaccard Distance ВидеоDemo lab: Curse of DimensionalityВнешний инструментCurse of Dimensionality Notebook - Part 1ВидеоCurse of Dimensionality Notebook - Part 2ВидеоCurse of Dimensionality Notebook - Part 3ВидеоCurse of Dimensionality Notebook - Part 4ВидеоPractice Lab: Distance Metrics LabВнешний инструментUngraded: Distance MetricsЗадание

Module Summary & Assessment

SummaryЧтениеGraded: Module 2 QuizЗадание
03Selecting a Clustering Algorithm18 материалов

Common clustering algorithms

Hierarchical Agglomerative Clustering ВидеоHierarchical Agglomerative Clustering: Hierarchical Linkage TypesВидеоApplying Hierarchical Agglomerative Clustering ВидеоDBSCAN ВидеоVisualizing DBSCAN ВидеоPractice lab: DBSCAN ClusteringВнешний инструментMean ShiftВидеоPractice lab: Mean Shift ClusteringВнешний инструментUngraded: Clustering AlgorithmsЗадание

Comparing clustering algorithms

Comparing AlgorithmsВидеоClustering Demo (Activity)Внешний инструментClustering Notebook - Part 1ВидеоClustering Notebook - Part 2Видео(Optional) Clustering Notebook - Part 3ВидеоClustering Notebook - Part 4Видео

Module Summary & Assessment

SummaryЧтениеGraded: Module 3 QuizЗадание
04Dimensionality Reduction12 материалов

Dimensionality Reduction

Dimensionality Reduction: OverviewВидеоDimensionality Reduction: Principal Component AnalysisВидео(Optional) Matrix ReviewВнешний инструментDemo lab: Dimensionality Reduction (Part 1)Внешний инструмент(Optional) Dimensionality Reduction Notebook - Part 1ВидеоDimensionality Reduction Notebook - Part 2ВидеоPractice lab: Principal Component AnalysisВнешний инструментSingular Value DecompositionВнешний инструментDimensionality Reduction Imaging ExampleВидеоUngraded: Dimensionality ReductionЗадание

Module Summary & Assessment

SummaryЧтениеGraded: Module 4 QuizЗадание
05Nonlinear and Distance-Based Dimensionality Reduction8 материалов

Kernel Principal Component Analysis and Multidimensional Scaling

Kernel Principal Component Analysis and Multidimensional ScalingВидеоDemo lab: Dimensionality Reduction (Part 2)Внешний инструментDimensionality Reduction Notebook - Part 3ВидеоPractice lab: Kernel PCAВнешний инструментPractice lab: Multidimensional ScalingВнешний инструментUngraded: Kernel PCA and MDSЗадание

Module Summary & Assessment

SummaryЧтениеGraded: Module 5 QuizЗадание
06Matrix Factorization9 материалов

Matrix Factorization

Non Negative Matrix FactorizationВидеоDemo lab: Non-Negative Matrix FactorizationВнешний инструмент(Optional) TF-IDF SupplementalВнешний инструментNon Negative Matrix Factorization Notebook - Part 1ВидеоNon Negative Matrix Factorization Notebook - Part 2ВидеоPractice lab: Non-Negative Matrix FactorizationВнешний инструментUngraded: Non Negative Matrix FactorizationЗадание

Module Summary & Assessment

SummaryЧтениеGraded: Module 6 QuizЗадание
07Final Project5 материалов

Course Final Project

Final Project OverviewЧтениеReading: Final Submission Guidelines and DeliverablesPLUGINOption 1: AI Graded - Final Project: Submission and EvaluationВнешний инструментOption 2: Peer Graded - Final Project Submission and EvaluationВзаимная проверка

Course Wrap-Up

Thanks from the Course TeamЧтение
Ungraded: Comparing Clustering AlgorithmsЗадание