Microsoft Azure Machine Learning Studio is a cloud-based integrated development environment (IDE) for building, training, and deploying machine-learning models on Microsoft Azure. This course typically involves learning how to use Azure Machine Learning Studio to develop and deploy machine learning solutions. The training may cover various aspects of the machine learning lifecycle, including data preparation, model development, training, evaluation, and deployment.

It's important to note that Microsoft Azure provides a variety of tools and services for machine learning, and Azure Machine Learning Studio is just one of them. The training might also cover other Azure machine learning services, such as Azure Machine Learning Service and Azure Machine Learning Compute. 

By the end of this course, participants should be able to:

• Understand the fundamentals of Azure Machine Learning, its key components, and its role in the machine learning lifecycle.
• Navigate and use the Azure Machine Learning Studio interface effectively.
• Learn techniques for importing, cleaning, and exploring datasets within Azure Machine Learning Studio.
• Explore the process of selecting and building machine learning models using Azure Machine Learning Studio's drag-and-drop interface.
• Understand how to use Azure Machine Learning Studio to train machine learning models on various types of data.
• Learn how to evaluate the performance of machine learning models using metrics and visualizations within the Azure Machine Learning Studio environment.
• Understand the importance of feature engineering and learn techniques for feature transformation and selection.
• Learn the process of deploying machine learning models as web services or APIs using Azure Machine Learning Studio.
• Understand how to monitor and manage deployed models, including versioning, logging, and troubleshooting.

Training will be conducted via Microsoft Team Meeting. Meeting invites will be shared on the day before the first day of training.

• 3 days (18 hours)

• Presentation Slides
• Training Recordings
• Study References

Upon successful completion of training, participants will receive a certificate.

This course is appropriate for a wide range of professionals but not limited to:

• Individuals interested in starting a career in RPA.
• Business analysts and professionals looking to automate routine tasks.
• IT professionals seeking to enhance their automation skills.
• Developers and programmers interested in RPA.

• Familiarity with fundamental machine learning concepts, including supervised and unsupervised learning, classification, regression, and clustering.
• Basic proficiency in a programming language such as Python or R, as many machine learning tasks involve coding.
• A foundational understanding of statistics and mathematics, particularly concepts related to probability, linear algebra, and calculus.
• Familiarity with data analysis techniques and tools, as participants may be working with datasets and performing exploratory data analysis.
• Basic familiarity with Microsoft Azure fundamentals, including an understanding of Azure services and how they are used in a cloud computing environment.
• Understanding of data preprocessing tasks such as cleaning, transformation, and feature engineering.
• Strong problem-solving skills and an analytical mindset are essential for effectively applying machine learning techniques to real-world scenarios.

Participants can avail a discount of either an early bird or group discount whichever is higher with an additional discount when signing up for 2 or more courses.

Group Discount (same company only)

• 15% Discount for groups of 5 or more
• 10% Discount for groups of 3-4

Bundle Discount

• Sign up for 2 courses and get an extra 10% off
• Sign up for 3 courses and get an extra 15% off