Machine Learning Engineer with Microsoft Azure | 2
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Overview
In this program, students will enhance their skills by building and deploying sophisticated Machine Learning (ML)
solutions using popular open source tools and frameworks such as scikit-learn. Using Azure Machine Learning’s
MLOps capabilities, students will gain experience in understanding their ML models, protecting people and their
data, and controlling the end-to-end ML lifecycle at scale. Gain practical experience by using the built-in Azure labs
accessible inside the Udacity classroom and run complex machine learning tasks for no additional cost.
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).
•
A background in beginning level statistics would also be helpful to understand and deploy the ML models.
•
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
•
If you’d like to prepare for this Nanodegree program, check out our
