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Untertitel: Englisch, Koreanisch, Vietnamesisch, Chinesisch (vereinfacht)

Kompetenzen, die Sie erwerben

Python ProgrammingMachine Learning ConceptsMachine LearningDeep Learning

Karriereergebnisse der Lernenden

32%

nahm einen neuen Beruf nach Abschluss dieser Kurse auf

30%

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. 15 Stunden zum Abschließen
Englisch
Untertitel: Englisch, Koreanisch, Vietnamesisch, Chinesisch (vereinfacht)

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University of Washington

Lehrplan - Was Sie in diesem Kurs lernen werden

InhaltsbewertungThumbs Up93%(46,261 Bewertungen)Info
Woche
1

Woche 1

3 Stunden zum Abschließen

Welcome

3 Stunden zum Abschließen
18 Videos (Gesamt 84 min), 8 Lektüren, 1 Quiz
18 Videos
Who we are5m
Machine learning is changing the world3m
Why a case study approach?7m
Specialization overview6m
How we got into ML3m
Who is this specialization for?4m
What you'll be able to do57
The capstone and an example intelligent application6m
The future of intelligent applications2m
Starting a Jupyter Notebook5m
Creating variables in Python7m
Conditional statements and loops in Python8m
Creating functions and lambdas in Python3m
Starting Turi Create & loading an SFrame4m
Canvas for data visualization4m
Interacting with columns of an SFrame4m
Using .apply() for data transformation5m
8 Lektüren
Important Update regarding the Machine Learning Specialization10m
Slides presented in this module10m
Getting started with Python, Jupyter Notebook, & Turi Create10m
Where should my files go?10m
Important changes from previous courses10m
Download the Jupyter Notebook used in this lesson to follow along10m
Download the Jupyter Notebook used in this lesson to follow along10m
Download Wiki People Data10m
1 praktische Übung
SFrames15m
Woche
2

Woche 2

2 Stunden zum Abschließen

Regression: Predicting House Prices

2 Stunden zum Abschließen
19 Videos (Gesamt 82 min), 3 Lektüren, 2 Quiz
19 Videos
What is the goal and how might you naively address it?3m
Linear Regression: A Model-Based Approach5m
Adding higher order effects4m
Evaluating overfitting via training/test split6m
Training/test curves4m
Adding other features2m
Other regression examples3m
Regression ML block diagram5m
Loading & exploring house sale data7m
Splitting the data into training and test sets2m
Learning a simple regression model to predict house prices from house size3m
Evaluating error (RMSE) of the simple model2m
Visualizing predictions of simple model with Matplotlib4m
Inspecting the model coefficients learned1m
Exploring other features of the data6m
Learning a model to predict house prices from more features3m
Applying learned models to predict price of an average house5m
Applying learned models to predict price of two fancy houses7m
3 Lektüren
Slides presented in this module10m
Download the Jupyter Notebook used in this lesson to follow along10m
Predicting house prices assignment10m
2 praktische Übungen
Regression18m
Predicting house prices6m
Woche
3

Woche 3

2 Stunden zum Abschließen

Classification: Analyzing Sentiment

2 Stunden zum Abschließen
19 Videos (Gesamt 75 min), 3 Lektüren, 2 Quiz
19 Videos
What is an intelligent restaurant review system?4m
Examples of classification tasks4m
Linear classifiers5m
Decision boundaries3m
Training and evaluating a classifier4m
What's a good accuracy?3m
False positives, false negatives, and confusion matrices6m
Learning curves5m
Class probabilities1m
Classification ML block diagram3m
Loading & exploring product review data2m
Creating the word count vector2m
Exploring the most popular product4m
Defining which reviews have positive or negative sentiment4m
Training a sentiment classifier3m
Evaluating a classifier & the ROC curve4m
Applying model to find most positive & negative reviews for a product4m
Exploring the most positive & negative aspects of a product4m
3 Lektüren
Slides presented in this module10m
Download the Jupyter Notebook used in this lesson to follow along10m
Analyzing product sentiment assignment10m
2 praktische Übungen
Classification14m
Analyzing product sentiment22m
Woche
4

Woche 4

2 Stunden zum Abschließen

Clustering and Similarity: Retrieving Documents

2 Stunden zum Abschließen
17 Videos (Gesamt 76 min), 3 Lektüren, 2 Quiz
17 Videos
What is the document retrieval task?1m
Word count representation for measuring similarity6m
Prioritizing important words with tf-idf3m
Calculating tf-idf vectors5m
Retrieving similar documents using nearest neighbor search2m
Clustering documents task overview2m
Clustering documents: An unsupervised learning task4m
k-means: A clustering algorithm3m
Other examples of clustering6m
Clustering and similarity ML block diagram7m
Loading & exploring Wikipedia data5m
Exploring word counts5m
Computing & exploring TF-IDFs7m
Computing distances between Wikipedia articles5m
Building & exploring a nearest neighbors model for Wikipedia articles3m
Examples of document retrieval in action4m
3 Lektüren
Slides presented in this module10m
Download the Jupyter Notebook used in this lesson to follow along10m
Retrieving Wikipedia articles assignment10m
2 praktische Übungen
Clustering and Similarity12m
Retrieving Wikipedia articles18m

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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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