DP-100: A-Z Machine Learning using Azure Machine Learning

DP-100: A-Z Machine Learning using Azure Machine Learning | Udemy
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

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

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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