Über diesen Kurs

76,660 kürzliche Aufrufe

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38%

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100 % online
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Ca. 14 Stunden zum Abschließen
Englisch
Untertitel: Englisch, Koreanisch, Arabischer Raum

Kompetenzen, die Sie erwerben

Data Clustering AlgorithmsK-Means ClusteringMachine LearningK-D Tree

Karriereergebnisse der Lernenden

37%

nahm einen neuen Beruf nach Abschluss dieser Kurse auf

38%

ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Ca. 14 Stunden zum Abschließen
Englisch
Untertitel: Englisch, Koreanisch, Arabischer Raum

von

University of Washington-Logo

University of Washington

Lehrplan - Was Sie in diesem Kurs lernen werden

InhaltsbewertungThumbs Up91%(5,386 Bewertungen)Info
Woche
1

Woche 1

1 Stunde zum Abschließen

Welcome

1 Stunde zum Abschließen
4 Videos (Gesamt 25 min), 4 Lektüren
4 Videos
Course overview3m
Module-by-module topics covered8m
Assumed background6m
4 Lektüren
Important Update regarding the Machine Learning Specialization10m
Slides presented in this module10m
Software tools you'll need for this course10m
A big week ahead!10m
Woche
2

Woche 2

4 Stunden zum Abschließen

Nearest Neighbor Search

4 Stunden zum Abschließen
22 Videos (Gesamt 137 min), 4 Lektüren, 5 Quiz
22 Videos
1-NN algorithm2m
k-NN algorithm6m
Document representation5m
Distance metrics: Euclidean and scaled Euclidean6m
Writing (scaled) Euclidean distance using (weighted) inner products4m
Distance metrics: Cosine similarity9m
To normalize or not and other distance considerations6m
Complexity of brute force search1m
KD-tree representation9m
NN search with KD-trees7m
Complexity of NN search with KD-trees5m
Visualizing scaling behavior of KD-trees4m
Approximate k-NN search using KD-trees7m
Limitations of KD-trees3m
LSH as an alternative to KD-trees4m
Using random lines to partition points5m
Defining more bins3m
Searching neighboring bins8m
LSH in higher dimensions4m
(OPTIONAL) Improving efficiency through multiple tables22m
A brief recap2m
4 Lektüren
Slides presented in this module10m
Choosing features and metrics for nearest neighbor search10m
(OPTIONAL) A worked-out example for KD-trees10m
Implementing Locality Sensitive Hashing from scratch10m
5 praktische Übungen
Representations and metrics12m
Choosing features and metrics for nearest neighbor search10m
KD-trees10m
Locality Sensitive Hashing10m
Implementing Locality Sensitive Hashing from scratch10m
Woche
3

Woche 3

2 Stunden zum Abschließen

Clustering with k-means

2 Stunden zum Abschließen
13 Videos (Gesamt 79 min), 2 Lektüren, 3 Quiz
13 Videos
An unsupervised task6m
Hope for unsupervised learning, and some challenge cases4m
The k-means algorithm7m
k-means as coordinate descent6m
Smart initialization via k-means++4m
Assessing the quality and choosing the number of clusters9m
Motivating MapReduce8m
The general MapReduce abstraction5m
MapReduce execution overview and combiners6m
MapReduce for k-means7m
Other applications of clustering7m
A brief recap1m
2 Lektüren
Slides presented in this module10m
Clustering text data with k-means10m
3 praktische Übungen
k-means18m
Clustering text data with K-means16m
MapReduce for k-means10m
Woche
4

Woche 4

3 Stunden zum Abschließen

Mixture Models

3 Stunden zum Abschließen
15 Videos (Gesamt 91 min), 4 Lektüren, 3 Quiz
15 Videos
Aggregating over unknown classes in an image dataset6m
Univariate Gaussian distributions2m
Bivariate and multivariate Gaussians7m
Mixture of Gaussians6m
Interpreting the mixture of Gaussian terms5m
Scaling mixtures of Gaussians for document clustering5m
Computing soft assignments from known cluster parameters7m
(OPTIONAL) Responsibilities as Bayes' rule5m
Estimating cluster parameters from known cluster assignments6m
Estimating cluster parameters from soft assignments8m
EM iterates in equations and pictures6m
Convergence, initialization, and overfitting of EM9m
Relationship to k-means3m
A brief recap1m
4 Lektüren
Slides presented in this module10m
(OPTIONAL) A worked-out example for EM10m
Implementing EM for Gaussian mixtures10m
Clustering text data with Gaussian mixtures10m
3 praktische Übungen
EM for Gaussian mixtures18m
Implementing EM for Gaussian mixtures12m
Clustering text data with Gaussian mixtures8m

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Über den Spezialisierung Maschinelles Lernen

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data....
Maschinelles Lernen

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