Course Summary
This course on Coursera is designed to teach students how to develop AI applications using Azure. It covers a range of topics including AI fundamentals, computer vision, natural language processing, and more.Key Learning Points
- Learn how to develop AI applications using Azure
- Gain knowledge on AI fundamentals, computer vision, and natural language processing
- Work on real-world projects to apply your skills
Related Topics for further study
- AI Application Development
- Azure Cloud Platform
- Computer Vision
- Natural Language Processing
- Machine Learning
Learning Outcomes
- Develop AI applications using Azure
- Apply AI fundamentals to real-world projects
- Build skills in computer vision and natural language processing
Prerequisites or good to have knowledge before taking this course
- Basic programming skills in Python
- Familiarity with machine learning concepts
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures
- Hands-on projects
Similar Courses
- AI for Everyone
- Machine Learning
- Introduction to Computer Vision
Related Education Paths
Notable People in This Field
- CEO of Microsoft
- Founder of deeplearning.ai
Related Books
Description
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.
Knowledge
- Define Artificial Intelligence and Machine Language
- Describe AI tools and roles, and the Microsoft Team Data Science Process
- Work with Azure APIs, including those for vision, language, and search
- Create, train, test and deploy your AI model in the cloud
Outline
- Introduction to Artificial Intelligence
- module 1 intro
- Definition of AI and Machine Learning
- Machine Learning Alogrithms
- Python Basics
- Python Collections
- Python Variables
- Python Scientific Ecosystem and ML Libraries
- Linear Regression with Scikit Learn
- Logistic Regression with Scikit Learn
- Module conclusion
- Exercise: Sign up for a free Azure account or login to your existing one.
- Definition of AI
- Comparison of machine learning algorithms
- Links to learn more about python
- Exercise Python Basics notebook
- Exercise: Scikit-learn models for regression and classification
- Practice: Python Collections
- AI and ML Definitions
- Deep Learning
- Module 1: Introduction to Artificial Intelligence
- Standardized AI Processes and Azure Resources
- module 2 intro
- AI Tools
- aiprocesses
- TDSP Stages
- TDSP General Manager Tasks
- TDSP Task Lead Tasks
- TDSP Project Lead Tasks
- TDSPData Scientist Tasks
- Module 2 Conclusion
- Microsoft Team Data Science Process
- Exercise: TDSP in Azure Devops
- Practice: TDSP
- ML Studio
- Module 2: Standardized AI Processes and Azure Resources
- Azure Cognitive APIs
- Module 3 Introduction
- Cognitive Services Overview
- Azure Computer Vision API
- face api
- Other Cognitive Services API's
- Sentiment analysis
- Module 3 Conclusion
- Exercise: Computer Vision Notebook
- Exercise: Face API Notebook
- Exercise: Sentiment Analysis Notebook
- Search API
- Translation
- Module 3: Azure Cognitive APIs
- Azure Machine Learning Service: Model Training
- Module 4 Introduction
- Azure ML Service
- Ways to create an ML Workspace
- Setting up Experiments
- Train and register a model
- Train a model using Azure ML
- Module 4 Conclusion
- Microsoft Azure Machine Learning Documentation
- Exercise: Workspace Notebook
- Exercise: Model Training Notebook
- Azure ML Process
- Practice: Azure ML Workspace
- Practice: Train
- Train Process Step
- Module 4: Azure Machine Learning Service
- Azure Machine Learning Service: Model Management and Deployment
- Module 5 Introduction
- Connect to your Workspace
- Reference your Registered Model
- defining scoring and dependencies
- Define Deployment Config
- Deploy Container Image
- Test the Deployed Image
- Module 5 Conclusion
- Exercise: Deployment Notebook
- Practice: Saving Connections
- Practice: SDK Container
- Model Registry
- Module 5: Azure Machine Learning Operations
Summary of User Reviews
Discover how to develop AI applications on Azure with this highly-rated course on Coursera. Students rave about the course's comprehensiveness, hands-on approach, and expert instructors. Many users found the practical exercises to be especially helpful.Key Aspect Users Liked About This Course
Practical exercisesPros from User Reviews
- Instructors are experts in the field
- Hands-on approach helps students learn quickly
- Course is very comprehensive
- Lots of practical exercises to reinforce learning
Cons from User Reviews
- Some users found the course material to be too advanced
- Course can be time-consuming
- Limited interaction with instructors
Ronald J. Daskevich, DCS
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