Azure Machine Learning Development: Part 1

1h 8mIntermediate2022-08-22

Authors

Zarina Meeran

Senior Cloud System Developer at Cloudreach | Azure, GCP, CCNA

Course details

Machine learning as a concept has been around for over 60 years, but as artificial intelligence becomes more and more popular, there’s increased opportunity in the field of artificial intelligence and its subgroups, machine learning and deep learning. And with the explosion of cloud computing, it’s only natural to study machine learning with cloud technologies like Azure. In this introductory course to her Azure machine learning series, Zarina Meeran starts you off with the basics of AI, and covers how ML and DL subgroups fit into the picture. She then dives into the various machine learning problems and the machine learning algorithms used to solve these problems. Zarina also covers the machine learning lifecycle to get the end-to-end picture, as well as the tools offered from Azure. Finally, she goes through the architecture of MLOps within the cloud as this is currently adopted.

Skills covered

Cloud DevelopmentMachine LearningAzurePersonaArtificial Intelligence (AI)Cloud ComputingMicrosoft

Concepts

0. Introduction

01 - Importance of Azure Machine Learning development

1. Artificial Intelligence (AI)

02 - What is artificial intelligence (AI)
03 - Benefits of AI

2. Machine Learning (ML)

04 - What is machine learning (ML)
05 - ML problem types
06 - Supervised learning
07 - Unsupervised learning
08 - Semisupervised learning
09 - Reinforcement learning

3. Machine Learning (ML) and Deep Learning (DL)

10 - What is deep learning (DL)
11 - ML vs. DL

4. Machine Learning Algorithms

12 - Clustering
13 - Classification
14 - Regression
15 - Anomaly detection

5. Machine Learning Lifecycle and Azure Machine Learning

16 - ML lifecycle
17 - Azure ML tools

6. Machine Learning Operations (MLOps)

18 - What is MLOps
19 - MLOps team roles

Conclusion

20 - Next steps