Azure Data Scientist Associate: Machine Learning
Azure 2021
| Intermediate
- 11 Videos | 1h 7m 57s
- Includes Assessment
- Earns a Badge
Machine Learning uses real data to train algorithms that can be used for anomaly detection, computer vision, and natural language processing. In this course, you'll learn about datasets and how to manipulate data for them. Next, you'll learn the difference between labeled and unlabeled data and why some AI models require labeled data. You'll examine the features that should be used for a selected dataset. Next, you'll learn about the types of machine learning algorithms that are available, including regression algorithms, classification algorithms, and clustering algorithms. Finally, you'll explore the difference between supervised and unsupervised machine learning models. This course is one in a collection that prepares learners for the Designing and Implementing a Data Science Solution on Azure (DP-100) exam.
WHAT YOU WILL LEARN
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discover the key concepts covered in this coursedescribe machine learning and how it can be used for anomaly detection, computer vision, and natural language processingdescribe datasets and how to manipulate data for those datasetsdescribe the difference between labeled and unlabeled data and why some AI models require labeled datadescribe how features are selected and used from datasets in AI algorithmsdescribe regression algorithms and how they are used to make predictions
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describe classification algorithms and how they are used to classify objects or relationsdescribe clustering algorithms and how they can be used to determine groupings in datadescribe how supervised machine learning models use labeled data, are simpler to build, and have more accurate resultsdescribe how unsupervised machine learning models discover patterns from unlabelled data and can perform complex processing taskssummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview1m 41sUP NEXT
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2.Machine Learning7m 55s
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3.Data Manipulation and Datasets7m 54s
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4.Labeled and Unlabeled Data5m 26s
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5.Data Features8m 35s
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6.Machine Learning Regression Algorithms7m 20s
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7.Machine Learning Classification Algorithms6m 27s
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8.Machine Learning Clustering Algorithms6m 43s
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9.Supervised Machine Learning7m 53s
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10.Unsupervised Machine Learning7m 19s
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11.Course Summary44s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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