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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.

Getting Started

Course Introduction

00:06:59

Machine Learning Essentials

What is Machine Learning?

00:21:34

Important Concepts in Machine Learning

00:21:00

Machine Learning Algorithms

00:23:30

What is Data Science - Part 1

00:09:46

What is Data Science - Part 2

00:10:56

Azure Data Science

00:03:24

Quiz: What is Machine Learning?

Getting started with Azure Machine Learning

About Azure Machine Learning

00:15:25

Using Azure Machine Learning Studio

00:12:01

Working with Modules in ML Studio

00:09:08

Working with Data in ML Studio

00:09:34

Demonstration: Download a Dataset and Summarize

00:11:06

Exercise: Sign into Azure ML Studio and Explore

00:30:00

Exercise: Import Data from a Local File

00:30:00

Exercise: Download Dataset from HTTP and Summarize.

00:30:00

Creating Predictive models using Azure ML

Machine Learning Modules

00:10:07

How to Choose a ML Algorithm

00:07:45

Demonstrate: Predict Automobile Price - Part 1

00:09:14

Demonstrate: Predict Automobile Price - Part 2

00:07:01

Exercise: Create Predictive Model for Predicting Price of an Automobile

00:30:00

Create Movie Recommender

Create a Movie Recommender - Import and Clean Data

00:09:45

Create a Movie Recommender - Split Data and Train Model

00:08:16

Predict Ratings for Users Using the Movie Recommender

00:04:07

Generate Recommendations for Users

00:03:05

Exercise: Train a Movie Recommendation Engine

00:30:00

Course Conclusion

Final Steps

What's Next?

00:02:24

Get Recognized!

00:01:01

Details

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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