課程信息

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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.

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完成後獲得證書

100% 在線

立即開始，按照自己的計劃學習。

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根據您的日程表重置截止日期。

高級

General knowledge of Azure

完成時間大約為16 小時

英語（English）

字幕：英語（English）

您將學到的內容有

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

您將獲得的技能

Python ProgrammingAzure AI modelsMicrosoft Team Data Sciences ProcessAzure Machine Learning ServiceAzure Machine Learning Workspace

可分享的證書

完成後獲得證書

100% 在線

立即開始，按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

高級

General knowledge of Azure

完成時間大約為16 小時

英語（English）

字幕：英語（English）

教學大綱 - 您將從這門課程中學到什麼

週

1

週 1

完成時間為 4 小時

Introduction to Artificial Intelligence

This module introduces Artificial Intelligence and Machine learning. Next, we talk about machine learning types and tasks. This leads into a discussion of machine learning algorithms. Finally we explore python as a popular language for machine learning solutions and share some scientific ecosystem packages which will help you implement machine learning. By the end of this unit you will be able to implement machine learning models in at least one of the available python machine learning libraries.

完成時間為 4 小時

10 個視頻（總計 47 分鐘）, 6 個閱讀材料, 4 個測驗

10 個視頻

module 1 intro43

Definition of AI and Machine Learning4分鐘

Machine Learning Alogrithms5分鐘

Python Basics1分鐘

Python Collections6分鐘

Python Variables2分鐘

Python Scientific Ecosystem and ML Libraries5分鐘

Linear Regression with Scikit Learn8分鐘

Logistic Regression with Scikit Learn9分鐘

Module conclusion1分鐘

6 個閱讀材料

Exercise: Sign up for a free Azure account or login to your existing one.30分鐘

Definition of AI10分鐘

Comparison of machine learning algorithms10分鐘

Links to learn more about python10分鐘

Exercise Python Basics notebook30分鐘

Exercise: Scikit-learn models for regression and classification40分鐘

4 個練習

Practice: Python Collections2分鐘

AI and ML Definitions6分鐘

Deep Learning3分鐘

Module 1: Introduction to Artificial Intelligence30分鐘

週

2

週 2

完成時間為 3 小時

Standardized AI Processes and Azure Resources

This module introduces machine learning tools available in Microsoft Azure. It then looks at standardized approaches developed to help data analytics projects to be successful. Finally, it gives you specific guidance on Microsoft's Team Data Science Approach to include roles and tasks involved with the process. The exercise at the end of this unit points you to Microsoft's documentation to implement this process in their DevOps solution if you don't have your own.

完成時間為 3 小時

9 個視頻（總計 44 分鐘）, 2 個閱讀材料, 3 個測驗

9 個視頻

module 2 intro39

AI Tools2分鐘

aiprocesses2分鐘

TDSP Stages7分鐘

TDSP General Manager Tasks4分鐘

TDSP Task Lead Tasks5分鐘

TDSP Project Lead Tasks5分鐘

TDSPData Scientist Tasks14分鐘

Module 2 Conclusion49

2 個閱讀材料

Microsoft Team Data Science Process10分鐘

Exercise: TDSP in Azure Devops1小時

3 個練習

Practice: TDSP6分鐘

ML Studio3分鐘

Module 2: Standardized AI Processes and Azure Resources30分鐘

週

3

週 3

完成時間為 3 小時

Azure Cognitive APIs

This module introduces you to Microsoft's pretrained and managed machine learning offered as REST API's in their suite of cognitive services. We specifically implement solutions using the computer vision api, the facial recognition api, and do sentiment analysis by calling the natural language service.

完成時間為 3 小時

7 個視頻（總計 28 分鐘）, 3 個閱讀材料, 3 個測驗

7 個視頻

Module 3 Introduction54

Cognitive Services Overview1分鐘

Azure Computer Vision API12分鐘

face api2分鐘

Other Cognitive Services API's2分鐘

Sentiment analysis6分鐘

Module 3 Conclusion1分鐘

3 個閱讀材料

Exercise: Computer Vision Notebook30分鐘

Exercise: Face API Notebook30分鐘

Exercise: Sentiment Analysis Notebook30分鐘

3 個練習

Search API3分鐘

Translation3分鐘

Module 3: Azure Cognitive APIs30分鐘

週

4

週 4

完成時間為 3 小時

Azure Machine Learning Service: Model Training

This module introduces you to the capabilities of the Azure Machine Learning Service. We explore how to create and then reference an ML workspace. We then talk about how to train a machine learning model using the Azure ML service. We talk about the purpose and role of experiments, runs, and models. Finally, we talk about

完成時間為 3 小時

7 個視頻（總計 37 分鐘）, 3 個閱讀材料, 5 個測驗

7 個視頻

Module 4 Introduction52

Azure ML Service1分鐘

Ways to create an ML Workspace14分鐘

Setting up Experiments2分鐘

Train and register a model5分鐘

Train a model using Azure ML11分鐘

Module 4 Conclusion1分鐘

3 個閱讀材料

Microsoft Azure Machine Learning Documentation10分鐘

Exercise: Workspace Notebook30分鐘

Exercise: Model Training Notebook1小時

5 個練習

Azure ML Process3分鐘

Practice: Azure ML Workspace6分鐘

Practice: Train3分鐘

Train Process Step3分鐘

Module 4: Azure Machine Learning Service30分鐘

常見問題

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I purchase the Certificate?

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?

You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.
Is financial aid available?

Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You’ll be prompted to complete an application and will be notified if you are approved. Learn more.

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Developing AI Applications on Azure