Download and Learn Become a Machine Learning Engineer for Microsoft Azure Udacity Nanodegree Course 2023 for free with google drive download link.

Strengthen your machine learning skills and build practical experience by training, validating, and evaluating models using Azure Machine Learning.

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What you will learn in Become a Machine Learning Engineer for Microsoft Azure Nanodegree

Machine Learning Engineer for Microsoft Azure

3 months to complete

In this program, students will enhance their skills by building and deploying sophisticated machine learning solutions using popular open source tools and frameworks, and gain practical experience running complex machine learning tasks using the built-in Azure labs accessible inside the Udacity classroom.

Become a Machine Learning Engineer for Microsoft Azure Nanodegree Intro Video:

Prerequisite knowledge

Prior experience with Python, Machine Learning, and Statistics

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A well-prepared learner will meet the following prerequisites:

  • Experience with basic Python programming (e.g., ability to read and write simple Python scripts; understanding of introductory concepts like variables, loops, modules, conditionals, data types, and functions).
  • Some experience with fundamental statistics and algebra, including an understanding of data distributions (e.g., normal distribution) measures of central tendency and variability (e.g., mean and standard deviation) and basic linear equations.
  • Udacity also recommends basic familiarity with fundamental machine learning concepts (such as feature engineering and supervised vs. unsupervised learning) and classic machine learning algorithms (such as linear regression and k-means clustering).
  • An understanding of the basics of Azure and Docker/Container experience.

Using Azure Machine Learning

Machine learning is a critical business operation for many organizations. Learn how to configure machine learning pipelines in Azure, identify use cases for Automated Machine Learning, and use the Azure ML SDK to design, create, and manage machine learning pipelines in Azure.

Optimizing an ML Pipeline In Azure

Throughout the course, we cover many different ways to work with data and machine learning. It can be quite challenging to decide what method to use – building your own machine learning pipeline, leveraging AutoML, hyperparameter tuning, and so on. In this project, students use scikit-learn, Hyperdrive, and AutoML to understand the costs and benefits of each methodology.

First, students will construct a pipeline from scikit-learn, using the Azure ML SDK to import data from a URL. Then, students will configure a Hyperdrive run for their scikit-learn pipeline to find the optimal hyperparameters. Students will then use the same dataset for an AutoML run to find an optimal model and set of hyperparameters. Finally, students write a README documenting their findings and comparing the differences, costs, and benefits of the different methods they’ve used.

Machine Learning Operations

This course covers a lot of the key concepts of operationalizing machine learning, from selecting the appropriate targets for deploying models, to enabling Application Insights, identifying problems in logs, and harnessing the power of Azure’s Pipelines. All these concepts are part of core DevOps pillars that will allow you to demonstrate solid skills for shipping machine learning models into production.

Operationalizing Machine Learning

MLOps and its core features have been covered in this course in detail. This

project will apply all the principles from the lessons to get a model trained with AutoML and deployed into a production environment. You will use Azure to configure a cloud-based machine learning production model, deploy it, and consume it. You will also create, publish, and consume a pipeline. In the end, you will demonstrate all of your work by creating a README file and a screencast video.

Capstone Project

The program capstone gives you the opportunity to use the knowledge you have obtained from this Nanodegree program to solve an interesting problem. You will have to use Azure’s Automated ML and HyperDrive to solve a task. Finally, you will have to deploy the model as a webservice and test the model endpoint.

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