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Introduction to Machine Learning using Microsoft Azure

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Free Online Course: Introduction to Machine Learning using Microsoft Azure provided by Udacity is a comprehensive online course, which lasts for 8 weeks long. The course is taught in English and is free of charge. Introduction to Machine Learning using Microsoft Azure is taught by Aniththa Umamahesan, Abe Omorogbe, Zoiner Tejada and Ciprian Jichici.

Microsoft via Udacity
8 weeks long
Self Paced
Overview

  • Gain a high-level introduction to the field of machine learning and prepare to use Azure Machine Learning Studio to train machine learning models. Plus, learn how to perform a variety of tasks on Azure Machine Learning labs — from data import, transformation and management to training, validating and evaluating models.

    Access to the Azure Machine Learning Labs will close after a predetermined number of students have completed the course.

Syllabus

    • Introduction to Machine Learning
      • Gain a high-level introduction to the field of machine learning and train your first machine learning model using Azure Machine Learning Studio
    • Model Training
      • Learn how to prepare data and then transform it into trained machine learning models and get an introduction to ensemble learning and automated machine learning
    • Supervised & Unsupervised Learning
      • Dive into two of Machine Learning's fundamental approaches: supervised and unsupervised learning and learn about classification, regression, clustering, representation learning, and more
    • Applications of Machine Learning
      • This lesson looks at some of the most important applications of ML, including deep learning, similarity learning, text classification, feature learning, and anomaly detection
    • Managed Services for Machine Learning
      • Learn how to enhance your ML processes with managed services and discuss computing resources, the modeling process, automation via pipelines, and more
    • Responsible AI
      • This lesson will tackle some of the potential implications and challenges posed by ML and AI—as well as principles for building responsible AI that avoids harming others

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