English | Size: 7.44 GB
Genre: eLearning
What you’ll learn
Prepare for and Pass the Azure DP-100 Exam
Master Data Science and Machine Learning Models using Azure ML.
Data Processing using Python and Pandas
AzureML SDK for Python for complete Machine Learning Lifecycle.
Azure Automated Machine Learning for faster and efficient Machine Learning model development and deployment
Understand the concepts and intuition of Machine Learning algorithms
Build Machine Learning models within minutes
Deploy production grade Machine Learning algorithms
Deploy Machine Learning webservices in the simplest manner
This course will help you and your team to build skills required to pass the most in demand and challenging, Azure DP-100 Certification exam. It will earn you one of the most in-demand certificate of Microsoft Certified: Azure Data Scientist Associate.
DP-100 is designed for Data Scientists. This exam tests your knowledge of Data Science and Machine learning to implement machine learning models on Azure. So you must know right from Machine Learning fundamentals, Python, planning and creating suitable environments in Azure, creating machine learning models as well as deploying them in production.
Why should you go for DP-100 Certification?
One of the very few certifications in the field of Data Science and Machine Learning.
You can successfully demonstrate your knowledge and abilities in the field of Data Science and Machine Learning.
You will improve your job prospects substantially in the field of Data Science and Machine Learning.
Key points about this course
Covers the most current syllabus as on 8th December, 2020.
100% syllabus of DP-100 Exam is covered.
Very detailed and comprehensive coverage with more than 200 lectures and 25 Hours of content
Crash courses on Python and Azure Fundamentals for those who are new to the world of Data Science
Machine Learning is one of the hottest and top paying skills. It’s also one of the most interesting field to work on.
In this course of Machine Learning using Azure Machine Learning, we will make it even more exciting and fun to learn, create and deploy machine learning models using Azure Machine Learning Service as well as the Azure Machine Learning Studio. We will go through every concept in depth. This course not only teaches basic but also the advance techniques of Data processing, Feature Selection and Parameter Tuning which an experienced and seasoned Data Science expert typically deploys. Armed with these techniques, in a very short time, you will be able to match the results that an experienced data scientist can achieve.
This course will help you prepare for the entry to this hot career path of Machine Learning as well as the Azure DP-100: Azure Data Scientist Associate exam.
—– Exam Syllabus for DP-100 Exam —–
1. Set up an Azure Machine Learning Workspace (30-35%)
Create an Azure Machine Learning workspace
Create an Azure Machine Learning workspaceConfigure workspace settings
Manage a workspace by using Azure Machine Learning studio
Manage data objects in an Azure Machine Learning workspace
Register and maintain datastores
Create and manage datasets
Manage experiment compute contexts
Create a compute instance
Determine appropriate compute specifications for a training workload
Create compute targets for experiments and training
Run Experiments and Train Models (25-30%)
Create models by using Azure Machine Learning Designer
Create a training pipeline by using Azure Machine Learning designer
Ingest data in a designer pipeline
Use designer modules to define a pipeline data flow
Use custom code modules in designer
Run training scripts in an Azure Machine Learning workspace
Create and run an experiment by using the Azure Machine Learning SDK
Configure run settings for a script
Consume data from a dataset in an experiment by using the Azure Machine Learning SDK
Generate metrics from an experiment run
Log metrics from an experiment run
Retrieve and view experiment outputs
Use logs to troubleshoot experiment run errors
Automate the model training process
Create a pipeline by using the SDK
Pass data between steps in a pipeline
Run a pipeline
Monitor pipeline runs
Optimize and Manage Models (20-25%)
Use Automated ML to create optimal models
Use the Automated ML interface in Azure Machine Learning studio
Use Automated ML from the Azure Machine Learning SDK
Select pre-processing options
Determine algorithms to be searched
Define a primary metric
Get data for an Automated ML run
Retrieve the best model
Use Hyperdrive to tune hyperparameters
Select a sampling method
Define the search space
Define the primary metric
Define early termination options
Find the model that has optimal hyperparameter values
Use model explainers to interpret models
Select a model interpreter
Generate feature importance data
Manage models
Register a trained model
Monitor model usage
Monitor data drift
Deploy and Consume Models (20-25%)
Create production compute targets
Consider security for deployed services
Evaluate compute options for deployment
Deploy a model as a service
Configure deployment settings
Consume a deployed service
Troubleshoot deployment container issues
Create a pipeline for batch inferencing
Publish a batch inferencing pipeline
Run a batch inferencing pipeline and obtain outputs
Publish a designer pipeline as a web service
Create a target compute resource
Configure an Inference pipeline
Consume a deployed endpoint
Who this course is for:
Developers who want to start a career in or wants to learn about the exciting domain of Data Science and Machine Learning
Existing Data Scientists who want to earn DP-100 Certification
Anyone who wants to learn Data Science and Machine Learning
Business Analysts who want to apply Data Science to solve business problems
Functional Experts who can take help of Machine Learning and build/test their hypothesis quickly
Students and non-technical professionals who want to start a career in Machine Learning
Data Engineers or Software Engineers who want to learn Data Science and Machine Learning
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