360° Microsoft Azure data science certification Course
The Microsoft Azure data science certification course by CCS Learning Academy comprises resources created by experienced trainers whare also industry experts. With our course, you will learn data science processes and ways to use a greater degree of automation in a seamless way.
Each course topic is broken into modules so that the students find it easier to remember the core subject matter. Microsoft Data Science certification starts with an introduction to Azure Machine Learning, followed by working on Machine Learning. We help students to get familiar with the Machine learning workspace and gradually move to the complicated parts of Azure which are running experiments, training models, and more.
What will you learn from our course?
CCS Learning Academy can help you to level up your knowledge and expertise in Data Science. With years of experience in the IT industry, we help aspirants stay up to date on Azure trends. Our course will help you to learn:
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By applying for the course you will learn to prepare yourself for the Microsoft exam and Data Engineering on Microsoft Azure.
Will learn operating machine learning solutions in the cloud using Azure machine learning.
Learn top programming languages like python and other essential languages.
Model training and deployment along with machine learning solution monitoring.
You will get the complete details of the course in the course topic section. Go through it and understand the modules we are providing. We have subdivided the modules according to the lessons we are going to provide for your in-depth understanding.
Knowledge required to get the Azure data science certification?
A candidate opting for this course or willing to be a part of the examination needs to have some knowledge and expertise in data science. They must know Azure Machine learning and MFlow.
You must also have ideas to define and prepare the development environment, perform feature engineering, develop models, and perform feature engineering.
Benefits of Azure DP-100 training
In today’s IT market, Microsoft Azure is one of the most popular cloud computing services that helps data scientists to design and implement solutions easily. CCS Learning Academy has created this self-paced course where learners can easily understand the degree of automation.
Mentioned below are a few benefits of enrolling in the Azure data science certification:
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Certification can prove beneficial for your career:
Data security helps companies in understanding multiple sources and valuable insights into data. Therefore, its protection is equally important and this is why data security courses provide bigger career prospects for developers.
Provide credibility to any job position: To prove credibility you need an accurate certificate and expertise. If you are a data science aspirant then the DP-100 examination can help you to become an associate and expert. The certification can also prove your dedication and commitment to the subject and make you a better professional. This course is the best one for candidates who want to initiate their journey in data security.
Validation of your skills and knowledge of data security: Microsoft Data science certification validates the skill and knowledge in machine learning and data science. It evaluates your candidature in the categories like managing resources for machine learning, running experiments, deploying machine learning solutions, implementing responsible machine learning, and much more
The Microsoft Azure data science certification by CCS Learning Academy qualifies you for your fundamental’s certification examination. Also, help you to become a qualified data scientist. Do enroll in our course if you are willing to align your skills with the azure architectural commitment.
Why choose the dp100 course from CCS Learning Academy?
CCS Learning Academy has years of experience in technology training. Indeed, we train our students under the guidance of years-old industry experts on LIVE projects. Also, the complete dp100 course is explained clearly and all modules are covered on time.
We ensure that after the completion of the course, each of the students is completely prepared for the examination. Azure data science certification requires accomplishing technical tasks like:
Preparing a machine-learning solution
Exploring data and training models
Preparing a model of deployment
Deploying and Retaining a model
CCS Learning academy believes that Azure provides better security offerings, and this is why this course is worth opting for. We have covered each module evenly and you can easily get ready for your examination. With the help of our instructor-led classes, resources, assessments, and exam materials it will definitely be easy.
Therefore, get in touch with us if you have got plans to enroll in the dp100 course in the future session. You can either give us a call or send an email for the details.
Course Topics
Module 1: Getting Started with Azure Machine Learning
In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.
Lessons
- Introduction to Azure Machine Learning
- Working with Azure Machine Learning
Lab : Create an Azure Machine Learning Workspace
After completing this module, you will be able to
- Provision an Azure Machine Learning workspace
- Use tools and code to work with Azure Machine Learning
Module 2: No-Code Machine Learning
This module introduces the Automated Machine Learning and Designer visual tools, which you can use to train, evaluate, and deploy machine learning models without writing any code.
Lessons
- Automated Machine Learning
- Azure Machine Learning Designer
Lab : Use Automated Machine Learning
Lab : Use Azure Machine Learning Designer
After completing this module, you will be able to
- Use automated machine learning to train a machine learning model
- Use Azure Machine Learning designer to train a model
Module 3: Running Experiments and Training Models
In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.
Lessons
- Introduction to Experiments
- Training and Registering Models
Lab : Run Experiments
Lab : Train Models
After completing this module, you will be able to
- Run code-based experiments in an Azure Machine Learning workspace
- Train and register machine learning models
Module 4: Working with Data
Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.
Lessons
- Working with Datastores
- Working with Datasets
Lab : Work with Data
After completing this module, you will be able to
- Create and use datastores
- Create and use datasets
Module 5: Working with Compute
One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you’ll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.
Lessons
- Working with Environments
- Working with Compute Targets
Lab : Work with Compute
After completing this module, you will be able to
- Create and use environments
- Create and use compute targets
Module 6: Orchestrating Operations with Pipelines
Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it’s time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you’ll explore how to define and run them in this module.
Lessons
- Introduction to Pipelines
- Publishing and Running Pipelines
Lab : Create a Pipeline
After completing this module, you will be able to
- Create pipelines to automate machine learning workflows
- Publish and run pipeline services
Module 7: Deploying and Consuming Models
Models are designed to help decision making through predictions, so they’re only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.
Lessons
- Real-time Inferencing
- Batch Inferencing
- Continuous Integration and Delivery
Lab : Create a Real-time Inferencing Service
Lab : Create a Batch Inferencing Service
After completing this module, you will be able to
- Publish a model as a real-time inference service
- Publish a model as a batch inference service
- Describe techniques to implement continuous integration and delivery
Module 8: Training Optimal Models
By this stage of the course, you’ve learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you’ll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.
Lessons
- Hyperparameter Tuning
- Automated Machine Learning
Lab : Tune Hyperparameters
Lab : Use Automated Machine Learning from the SDK
After completing this module, you will be able to
- Optimize hyperparameters for model training
- Use automated machine learning to find the optimal model for your data
Module 9: Responsible Machine Learning
Data scientists have a duty to ensure they analyze data and train machine learning models responsibly; respecting individual privacy, mitigating bias, and ensuring transparency. This module explores some considerations and techniques for applying responsible machine learning principles.
Lessons
- Differential Privacy
- Model Interpretability
- Fairness
Lab : Explore Differential privacy
Lab : Interpret Models
Lab : Detect and Mitigate Unfairness
After completing this module, you will be able to
- Apply differential privacy to data analysis
- Use explainers to interpret machine learning models
- Evaluate models for fairness
Module 10: Monitoring Models
After a model has been deployed, it’s important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.
Lessons
- Monitoring Models with Application Insights
- Monitoring Data Drift
Lab : Monitor a Model with Application Insights
Lab : Monitor Data Drift
After completing this module, you will be able to
- Use Application Insights to monitor a published model
- Monitor data drift
Target Audience
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.
Prerequisites
Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.
Specifically:
- Creating cloud resources in Microsoft Azure.
- Using Python to explore and visualize data.
- Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
- Working with containers
To gain these prerequisite skills, take the following free online training before attending the course:
If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.
Inclusions
With CCS Learning Academy, you’ll receive:
- 3 Day Certified Instructor-led training
- Official Training Seminar Student Handbook
- Collaboration with classmates (not currently available for self-paced course)
- Real-world learning activities and scenarios
- Exam scheduling support*
- Enjoy job placement assistance for the first 12 months after course completion.
- This course is eligible for CCS Learning Academy’s Learn and Earn Program: get a tuition fee refund of up to 50% if you are placed in a job through CCS Global Tech’s Placement Division*
- Government and Private pricing available.*
*For more details call:Â 858-208-4141Â or email:Â training@ccslearningacademy.com; sales@ccslearningacademy.com
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