In this Data Science microcredential course, learners will embark on an exploration of the expansive domain of machine learning, harnessing the capabilities of Azure Machine Learning. Whether one is seeking to augment their existing proficiency in Python and machine learning or is taking their first steps in this field, this microcredential course serves as a guiding beacon, addressing pivotal facets such as data acquisition, preprocessing, model training, and deployment. Furthermore, participants will acquire proficiency in the effective monitoring of machine learning solutions within the Microsoft Azure ecosystem. This meticulously crafted course is designed to equip individuals with the skills and expertise required to fully leverage the potential of Azure in the realm of machine learning, thereby facilitating their journey towards becoming adept practitioners in the field of data science.
- Microsoft Azure
- Machine learning
- Data Scientist
- AI Engineer
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
Laptop, Intel Core i5 or higher, 16GB, 1TB Storage, Graphics Card (Hardware); Microsoft Azure (Software); Adequate Internet Connection (Network)
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.
In this Data Science course, there will be
- One (1) diagnostic assessment is to be conducted synchronously and is knowledge-based, allowing learners to choose between remote or on-site participation.
- Two (2) formative assessments, one focused on knowledge and the other on performance. Both are available asynchronously, allowing learners to complete them at their own pace and participate remotely or on-site.
- A performance-based summative assessment is conducted synchronously, providing learners remote or on-site participation.
Credit and Recognition
Upon completing the Machine Learning course, learners will receive a Certificate of Completion and a badge. These serve both as a recognition of acquired data science expertise and a foundational step toward more advanced studies in said field.
This course is facilitated by a Microsoft Certified Professional, someone who has completed professional training for Microsoft products through a certification program provided by Microsoft. To ensure the quality of this microcredential, continuous feedback loops with students, instructors, and industry practitioners are maintained to continuously improve content, delivery, and assessment methods.
The learner is eligible to take the Microsoft Certified: Azure Data Scientist Associate.
Data Science Path. 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 predictive modeling and artificial intelligence.