46 HOURS
ONLINE
ENGLISH
In this Data Science course, we will explore the vast field of machine learning leveraging the potential of Azure Machine Learning. Whether you’re building on your foundational knowledge of Python and machine learning or just getting started, this course will guide you through important aspects such as data ingestion, preparation, model training, and deployment. Plus, you’ll learn how to monitor machine learning solutions proficiently within the Microsoft Azure environment. It’s a course designed to give you the skills and knowledge to harness the full power of Azure in machine learning, helping you become proficient in data science.
At the end of this course, learners will be able to describe what data science and machine learning are, including their applications & use cases, and the various types of tasks performed by data scientists. They will also be proficient in examining the Azure Machine Learning SDK tool. Furthermore, learners will be equipped to deploy, train, and analyze a model using Azure Machine Learning.
- Microsoft Azure
- Machine learning
- Data Scientist
- AI Engineer
At the end of this course, learners will be able to describe what data science and machine learning are, including their applications & use cases, and the various types of tasks performed by data scientists. They will also be proficient in examining the Azure Machine Learning SDK tool. Furthermore, learners will be equipped to deploy, train, and analyze a model using Azure Machine Learning.
- Microsoft Azure
- Machine learning
- Data Scientist
- AI Engineer
Course Outline
Module 1: Design a Machine Learning Solution
Module 2: Exploring Azure Machine Learning
Module 3: Data Management in Azure ML
Module 4: Working with Azure ML Compute Resources
Module 5: No-Code Approaches in Azure ML
Module 6: Automation in Model Selection
Module 7: Using Notebooks in Azure ML
Module 8: Training Models using Scripts
Module 9: Optimization Techniques in Azure ML
Module 10: Model Management Techniques
Module 11: Model Deployment and Consumption
Module 12: Machine Learning Operations (MLOps) in Azure
Required Resources
Laptop, Intel Core i5 or higher, 16GB, 1TB Storage, Graphics Card (Hardware); Microsoft Azure (Software); Adequate Internet Connection (Network)
Pre-Requisites
This course is designed 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.
Assessment
One (1) diagnostic assessment is available, conducted synchronously, and is knowledge-based, with the flexibility for learners to choose between remote or on-site participation.
Two (2) formative assessments are offered, one focused on knowledge and the other on performance. Both are available asynchronously, allowing learners to complete them at their own pace, and learners have the option to participate remotely or on-site.
A performance-based summative assessment is to be conducted synchronously, providing learners with the choice of remote or on-site participation.
Credit and Recognition
The learner is eligible to take the Microsoft Certified: Azure Data Scientist Associate.
This course is facilitated by a Microsoft Certified professional. To ensure the quality of this micro-credential, continuous feedback loops with students, instructors, and industry practitioners are maintained to continually improve content, delivery, and assessment methods.
Learning Pathways
Data Science is the ultimate destination, and it can be reached through two distinct paths:
Data Analytics: If you’re passionate about discovering actionable insights from data.
Machine Learning: If you’re intrigued by the world of predictive modeling and artificial intelligence.
