DP-100: A-Z Machine Learning using Azure Machine Learning (Udemy.com)

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 May, 2021.

• 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

Some feedback from previous students,

1. "The instructor explained every concept smoothly and clearly. I'm an acountant without tech background nor excellent statistical knowledge. I do really appreciate these helpful on-hand labs and lectures. Passed the DP-100 in Dec 2020. This course really help."