Azure Data Scientist Associate: Machine Learning

Azure    |    Intermediate
  • 11 videos | 1h 7m 57s
  • Includes Assessment
  • Earns a Badge
Rating 4.6 of 40 users Rating 4.6 of 40 users (40)
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

  • Discover the key concepts covered in this course
    Describe machine learning and how it can be used for anomaly detection, computer vision, and natural language processing
    Describe datasets and how to manipulate data for those datasets
    Describe the difference between labeled and unlabeled data and why some ai models require labeled data
    Describe how features are selected and used from datasets in ai algorithms
    Describe regression algorithms and how they are used to make predictions
  • Describe classification algorithms and how they are used to classify objects or relations
    Describe clustering algorithms and how they can be used to determine groupings in data
    Describe how supervised machine learning models use labeled data, are simpler to build, and have more accurate results
    Describe how unsupervised machine learning models discover patterns from unlabelled data and can perform complex processing tasks
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 41s
    This course explores how to manipulate datasets, and examines AI model data, data types for machine learning algorithms, regression algorithms, prediction classification algorithms, clustering algorithms, and supervised versus unsupervised machine learning models. FREE ACCESS
  • 7m 55s
    Explore machine learning, find out what it is and how it can be used to detect anomalies, support computer vision, and process natural language. Compare traditional machine learning, which expects all data to be in structured formats, with deep learning, which employs neural networks. FREE ACCESS
  • Locked
    3.  Data Manipulation and Datasets
    7m 54s
    When it comes to machine learning (ML), one of the most important things you can have, if not the most important thing, is data sets. This course describes datasets and how to manipulate data for those datasets. See how ML can determine answers to questions based on limited information. FREE ACCESS
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    4.  Labeled and Unlabeled Data
    5m 26s
    Discover the difference between labeled and unlabeled data and why some AI models require labeled data. Compare how supervised machine learning (ML) needs labeled data to be trained, but unsupervised ML does not need labeled data: Instead, it tries on its own to derive meaning from the data. FREE ACCESS
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    5.  Data Features
    8m 35s
    When we explore datasets that we use for modeling, we sometimes refer to their features, but just what is a feature? Discover how features are selected and used from datasets in AI algorithms. Learn the purpose of feature engineering. FREE ACCESS
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    6.  Machine Learning Regression Algorithms
    7m 20s
    ML algorithms boil down to three main categories: regression, classification, and clustering. In this video, discover how regression algorithms are used to make predictions. Learn the benefits of regression algorithms. Compare linear, logistic, stepwise, and ridge regression methods. FREE ACCESS
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    7.  Machine Learning Classification Algorithms
    6m 27s
    Observe how classification algorithms use classification models to predict decisions. Compare multiclass classification with multilabel classification. Consider linear classifiers, such as logistic regression, the Naive Bayes classifier, and Fisher's linear discriminant. FREE ACCESS
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    8.  Machine Learning Clustering Algorithms
    6m 43s
    Describe clustering algorithms and how they can be used to determine groupings in data, such as for car sizes, styles, or weights. Compare three cluster algorithms types: centroid, hierarchical, and distribution-based. FREE ACCESS
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    9.  Supervised Machine Learning
    7m 53s
    Explore how supervised machine learning relies on well understood input data sets to train their models, so that the models can analyze all the features of their data sets and derive algorithms that come to the same conclusions. FREE ACCESS
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    10.  Unsupervised Machine Learning
    7m 19s
    Describe how unsupervised machine learning models discover patterns from unlabeled data and can perform complex processing tasks. Discover when unsupervised machine learning has advantage over supervised machine learning. FREE ACCESS
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    11.  Course Summary
    44s
    This course explored how to manipulate datasets, and examined AI model data, data types for machine learning algorithms, regression algorithms, prediction classification algorithms, clustering algorithms, and supervised versus unsupervised machine learning models. FREE ACCESS

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