- Level Expert
- Duration 16 hours
- Course by LearnQuest
-
Offered by
About
This course introduces the concepts of Artificial Intelligence and Machine learning. We'll discuss machine learning types and tasks, and machine learning algorithms. You'll explore Python as a popular programming language for machine learning solutions, including using some scientific ecosystem packages which will help you implement machine learning. Next, this course introduces the machine learning tools available in Microsoft Azure. We'll review standardized approaches to data analytics and you'll receive specific guidance on Microsoft's Team Data Science Approach. As you go through the course, we'll introduce you to Microsoft's pre-trained and managed machine learning offered as REST API's in their suite of cognitive services. We'll implement solutions using the computer vision API and the facial recognition API, and we'll do sentiment analysis by calling the natural language service. Using the Azure Machine Learning Service you'll create and use an Azure Machine Learning Worksace.Then you'll train your own model, and you'll deploy and test your model in the cloud. Throughout the course you will perform hands-on exercises to practice your new AI skills. By the end of this course, you will be able to create, implement and deploy machine learning models.Modules
Module Introduction
1
Videos
- module 1 intro
1
Readings
- Exercise: Sign up for a free Azure account or login to your existing one.
Definition of AI and machine learning
1
Discussions
- Application of AI and Machine Learning
1
Videos
- Definition of AI and Machine Learning
1
Readings
- Definition of AI
Machine Learning Algorithms
1
Videos
- Machine Learning Alogrithms
1
Readings
- Comparison of machine learning algorithms
Python Basics
2
Assignment
- Practice: Python Collections
- AI and ML Definitions
3
Videos
- Python Basics
- Python Collections
- Python Variables
2
Readings
- Links to learn more about python
- Exercise Python Basics notebook
Scientific Python Ecosystem and Machine Learning Libraries
1
Assignment
- Deep Learning
3
Videos
- Python Scientific Ecosystem and ML Libraries
- Linear Regression with Scikit Learn
- Logistic Regression with Scikit Learn
1
Readings
- Exercise: Scikit-learn models for regression and classification
Module Conclusion
1
Assignment
- Module 1: Introduction to Artificial Intelligence
1
Videos
- Module conclusion
Module 2 Intro
1
Videos
- module 2 intro
AI Tools
1
Videos
- AI Tools
Standardized AI processes and Azure Resources
1
Discussions
- Data Science Project Management
1
Videos
- aiprocesses
TDSP steps in detail
1
Videos
- TDSP Stages
1
Readings
- Microsoft Team Data Science Process
TDSP suggested roles
2
Assignment
- Practice: TDSP
- ML Studio
4
Videos
- TDSP General Manager Tasks
- TDSP Task Lead Tasks
- TDSP Project Lead Tasks
- TDSPData Scientist Tasks
1
Readings
- Exercise: TDSP in Azure Devops
Module 2 Conclusion
1
Assignment
- Module 2: Standardized AI Processes and Azure Resources
1
Videos
- Module 2 Conclusion
Module 3 Introduction
1
Videos
- Module 3 Introduction
Azure Cognitive Services Overview
1
Discussions
- Practical Application of Cognitive API's
1
Videos
- Cognitive Services Overview
Azure Computer Vision API
1
Assignment
- Search API
2
Videos
- Azure Computer Vision API
- face api
2
Readings
- Exercise: Computer Vision Notebook
- Exercise: Face API Notebook
Other Available APIs
1
Videos
- Other Cognitive Services API's
Sentiment Analysis with the Text Analytics API
1
Assignment
- Translation
1
Videos
- Sentiment analysis
1
Readings
- Exercise: Sentiment Analysis Notebook
Module 3 Conclusion
1
Assignment
- Module 3: Azure Cognitive APIs
1
Videos
- Module 3 Conclusion
Module 4 Introduction
1
Videos
- Module 4 Introduction
What is Azure ML Service?
1
Videos
- Azure ML Service
1
Readings
- Microsoft Azure Machine Learning Documentation
Creating an Azure ML Workspace
1
Assignment
- Azure ML Process
1
Videos
- Ways to create an ML Workspace
1
Readings
- Exercise: Workspace Notebook
Experiments, Runs, and Models
1
Assignment
- Practice: Azure ML Workspace
1
Videos
- Setting up Experiments
Develop and register a model
1
Assignment
- Practice: Train
1
Videos
- Train and register a model
Train a model on the Cloud
1
Assignment
- Train Process Step
1
Videos
- Train a model using Azure ML
1
Readings
- Exercise: Model Training Notebook
Module 4 Conclusion
1
Assignment
- Module 4: Azure Machine Learning Service
1
Videos
- Module 4 Conclusion
Module 5 Introduction
1
Videos
- Module 5 Introduction
Connect to your Workspace
1
Assignment
- Practice: Saving Connections
1
Videos
- Connect to your Workspace
Reference your registered Model
1
Videos
- Reference your Registered Model
Prepare to Deploy
1
Videos
- defining scoring and dependencies
Container image and Deployment config
1
Videos
- Define Deployment Config
Deploy Container image
1
Assignment
- Practice: SDK Container
1
Videos
- Deploy Container Image
Sending JSON Requests to the Deployed Model
1
Assignment
- Model Registry
1
Videos
- Test the Deployed Image
1
Readings
- Exercise: Deployment Notebook
Module 5 Conclusion
1
Assignment
- Module 5: Azure Machine Learning Operations
1
Videos
- Module 5 Conclusion
Auto Summary
"Developing AI Applications on Azure" is an expert-level course focused on Artificial Intelligence and Machine Learning within the Data Science & AI domain. Taught by Coursera, it covers machine learning types, algorithms, and Python programming with scientific packages. Learners will explore Microsoft's Azure tools, including pre-trained machine learning models and REST APIs for computer vision, facial recognition, and sentiment analysis. The course includes hands-on exercises and spans 960 minutes, available through Starter and Professional subscriptions, ideal for those looking to harness Azure for AI solutions.

Ronald J. Daskevich, DCS