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Machine Learning with Azure

Training Architect
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Terrence Cox
A veteran of twenty years in Information Technology in a variety of roles. He has worked in development, security and infrastructure well before they merged into what we now call DevOps. He provides training in Linux, VMWare, DevOps (Ansible, Jenkins, etc) as well as containers and AWS topics.


This course begins with explaining the need of Machine Learning and how it originated from Aritificial Intelligence and gave rise to deep learning. We explain important concepts in ML including categories of algorithms, statistical and computer science terms used in model creation, feature engineering, overfitting, generalization, underfitting and cross validation. We also dive into the topic of data science and discuss why ML is an important part of data science.

The course then provides hands on training on Azure Machine Learning, giving a tour of ML Studio, its various features and the concept of an experiment. We demonstrate the process of creating ML experiments and create predictive models to predice automobile prices and generate recommendations for movies.

The exercises in this course allow the student to get familiar with Azure Machine Leaning and gain confidence in exploring the tool further.

Study Guides

Machine Learning with Azure

This guide presents the slides used in the course "Machine Learning with Azure".

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