Microsoft Azure Machine Learning DP-100

Microsoft Azure Machine Learning – DP-100, available at $49.99, has an average rating of 3.85, with 45 lectures, based on 62 reviews, and has 300 subscribers.

You will learn about Students of this course will be able to use the Azure Machine Learning as professionals This course is a good resource for people who would like to take the azure DP-100 certification You will be able to use Azure ML either using SDK code or in a non-code graphical model Understand the structure and the concepts of the Azure ML This course is ideal for individuals who are Anyone who would like to be introduced to machine learning using cloud or Anyone who wants to take his machine learning knowledge to a new level It is particularly useful for Anyone who would like to be introduced to machine learning using cloud or Anyone who wants to take his machine learning knowledge to a new level.

Enroll now: Microsoft Azure Machine Learning – DP-100

Summary

Title: Microsoft Azure Machine Learning – DP-100

Price: $49.99

Average Rating: 3.85

Number of Lectures: 45

Number of Published Lectures: 45

Number of Curriculum Items: 45

Number of Published Curriculum Objects: 45

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Students of this course will be able to use the Azure Machine Learning as professionals

This course is a good resource for people who would like to take the azure DP-100 certification

You will be able to use Azure ML either using SDK code or in a non-code graphical model

Understand the structure and the concepts of the Azure ML

Who Should Attend

Anyone who would like to be introduced to machine learning using cloud

Anyone who wants to take his machine learning knowledge to a new level

Target Audiences

Anyone who would like to be introduced to machine learning using cloud

Anyone who wants to take his machine learning knowledge to a new level

In this course about Azure Machine Learning bootcamp, you will learn the concepts of the Azure Machine learning from a professional point of view.

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This course lies mainly on the handling and the manipulation of Azure Machine Learning using python Software Development Kit (SDK). Interacting with Azure ML using the SDK is exactly what you will find in a professional environment.

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This course will take your skills in Azure ML to a new level. The content of this course covers most of (if it is not all) what you will find in the Microsoft Azure DP-100 certification. Having such certification will be definitively a boost of you career and salary. Machine Learning, especially when combined with cloud capabilities, is one of the most valuable skills in the market.

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This course has the particularity of being build to simulate true development tasks since the videos were recorded in a way to show you what you may encounter as problems (issues) and the way to correct them.

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Before I wish you a great journey with us, let please mention that our team will be tremendously happy to answer all your questions and bring clarifications to you.

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Whish you the best luck. See you in the first lecture !

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome and course overview

Lecture 2: Prerequisites

Lecture 3: What is Azure Machine Learning

Lecture 4: Setting up an Azure account

Lecture 5: [lab] Setting up an Azure account

Lecture 6: How to keep using Azure after you account expires

Lecture 7: [lab] Software installation: Anaconda

Lecture 8: [lab] Software installation: Visual Studio code

Lecture 9: [lab] Software installation: Docker desktop

Lecture 10: [lab] Interacting with Azure ML

Lecture 11: [Important Note]

Chapter 2: Workspaces

Lecture 1: What is a workspace in azure ML

Lecture 2: [lab] Creation of a workspace using the graphical interface

Lecture 3: [lab] Creation of a workspace using Azure ML SDK

Lecture 4: [lab] Creation of a workspace using CLI

Chapter 3: Experiments

Lecture 1: What is an experiment in Azure ML

Lecture 2: [Lab] Working with experiments – Part I

Lecture 3: [Lab] Working with experiments – Part II

Chapter 4: Estimators

Lecture 1: What is an estimator is Azure ML

Lecture 2: [lab] Working with estimators – Part I

Lecture 3: [lab] Working with estimators – Part II

Lecture 4: [lab] Working with estimators – Part III

Chapter 5: Datastores and Data

Lecture 1: Datastores

Lecture 2: [lab] Creation of datastores using the graphical interface

Lecture 3: [lab] Creation of datastores using Azure ML SDK

Lecture 4: [lab] Working with datasets – Part I

Lecture 5: [lab] Working with datasets – Part II

Chapter 6: Environments

Lecture 1: [lab] Working with environments

Chapter 7: Compute targets

Lecture 1: Compute targets

Lecture 2: [lab] Creation of compute targets using the graphical interface

Lecture 3: [lab] Creation of compute targets using SDK

Chapter 8: Hyperparameter tuning, Auto ML and model explanation

Lecture 1: Hyperparameters selection

Lecture 2: [lab] Hyperparameters selection

Lecture 3: Auto ML

Lecture 4: Note

Lecture 5: [lab] Auto ML

Chapter 9: Using Azure Machine Learning Designer

Lecture 1: [lab] Azure Machine Learning Designer – Part I

Lecture 2: [lab] Azure Machine Learning Designer – Part II

Chapter 10: Deployment and Monitoring

Lecture 1: Model deployment

Lecture 2: [lab] Deploy your model as a web service – Part I

Lecture 3: [lab] Deploy your model as a web service – Part II

Lecture 4: [lab] Deploy your model using the graphical interface

Lecture 5: [lab] Monitor you Machine Learning model

Chapter 11: Conclusion

Lecture 1: Resources to prepare your exam

Lecture 2: Concluding remarks

Instructors

Data Science Training
Machine Learning and Cloud Engineer

Rating Distribution

1 stars: 3 votes

2 stars: 4 votes

3 stars: 16 votes

4 stars: 23 votes

5 stars: 16 votes

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