Azure Data Scientist Associate: Machine Learning

IN THIS COURSE

• 1.  Course Overview
  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
• 2.  Machine Learning
  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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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