- Level Professional
- المدة 11 ساعات hours
- الطبع بواسطة Microsoft
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Offered by
عن
Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier. In this course, you will learn how to use Azure Machine Learning to create and publish models without writing code. This is the second course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Each course teaches you the concepts and skills that are measured by the exam. This Specialization is intended for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. It teaches data scientists how to create end-to-end solutions in Microsoft Azure. Students will learn how to manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions, and implement responsible machine learning. They will also learn to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning.الوحدات
Welcome to the Course
1
Discussions
- How Does Machine Learning Influence Your Day-to-Day Life?
1
Videos
- Introduction to Create No-code Predictive Models with Azure Machine Learning
2
Readings
- Course Syllabus
- How to be successful in this course
Azure Machine Learning to Train and Deploy a Predictive Model
2
Assignment
- Exercise Quiz
- Test Prep
2
Videos
- Azure Machine Learning to Train and Deploy a Predictive Model
- Weekly Summary
6
Readings
- Exercise Part 1: Create a Microsoft Azure Machine Learning Workspace
- Exercise Part 2: Create Compute Resources
- Exercise Part 3: Explore Data
- Exercise Part 4: Train a Machine Learning Model
- Exercise Part 5: Deploy a Model as a Service
- Exercise Part 6: Clean-up
1
Quiz
- Knowledge Check
Create a Regression Model with Azure Machine Learning Designer
2
Assignment
- Exercise Quiz
- Knowledge Check
2
Videos
- What is Regression?
- Weekly Summary
8
Readings
- Exercise Part 1: Create a Microsoft Azure Machine Learning Workspace
- Exercise Part 2: Create Compute Resources
- Exercise Part 3: Explore Data
- Exercise Part 4: Create and Run a Training Pipeline
- Exercise Part 5: Evaluate a Regression Model
- Exercise Part 6: Create an Inference Pipeline
- Exercise Part 7: Deploy a Predictive Service
- Exercise Part 8: Clean-up
1
Quiz
- Test Prep
Create a Classification Model with Azure Machine Learning Designer
3
Assignment
- Exercise Quiz
- Knowledge Check
- Test Prep
2
Videos
- What is Classification?
- Weekly Summary
8
Readings
- Exercise Part 1: Create an Azure Machine Learning Workspace
- Exercise Part 2: Create Compute Resources
- Exercise Part 3: Explore Data
- Exercise Part 4: Create and Run a Training Pipeline
- Exercise Part 5: Evaluate a Classification Model
- Exercise Part 6: Create an Inference Pipeline
- Exercise Part 7: Deploy a predictive service
- Exercise Part 8: Clean-up
Create a Clustering Model with Azure Machine Learning Designer
3
Assignment
- Exercise Quiz
- Knowledge Check
- Test Prep
2
Videos
- What is Clustering?
- Weekly Summary
8
Readings
- Exercise: Create a Microsoft Azure Machine Learning workspace
- Exercise Part 1: Create Compute Resources
- Exercise Part 2: Explore Data
- Exercise Part 3: Create and Run a Training Pipeline
- Exercise Part 4: Evaluate a Classification Model
- Exercise Part 5: Create an Inference Pipeline
- Exercise Part 6: Deploy a Predictive Service
- Exercise Part 7: Clean-up
Course Wrap-Up
1
Discussions
- Course Review
1
Videos
- Course wrap-up
1
Readings
- What to expect next
Auto Summary
Unlock the potential of machine learning in the cloud with the "Microsoft Azure Machine Learning for Data Scientists" course. Perfect for data science professionals, this course delivers comprehensive training on using Azure Machine Learning to build and deploy predictive models without the need to write code. As part of a five-course series designed to prepare you for the DP-100 certification exam, this course covers critical skills such as managing data ingestion and preparation, model training, deployment, and monitoring machine learning solutions. Leveraging your existing knowledge of Python and popular machine learning frameworks like Scikit-Learn, PyTorch, and TensorFlow, you'll gain expertise in creating end-to-end machine learning solutions on Microsoft Azure. Throughout the course, you'll explore how to manage Azure resources, run experiments, and implement responsible machine learning practices. Additionally, you will learn to use Azure Databricks for data exploration, preparation, and modeling, and integrate these processes seamlessly with Azure Machine Learning. This professional-level course, offered by Coursera, spans 660 hours and is available under the Starter subscription plan. It’s ideal for data scientists looking to enhance their skills and operate machine learning solutions at a cloud scale, preparing you to master the Azure platform and achieve the DP-100 certification. Join now to advance your career and stay ahead in the rapidly evolving field of data science and AI.

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