Dp-100 Azure Data Scientist Associate Complete Exam Guide

Complete DP-100 Azure Machine Learning training guide to prepare you for DP-100, with practice exams on DP 100 Azure ML

What you'll learn

Everything you need to pass the DP-100 exam and receive the Azure Data Scientist Associate certification

All learning objectives found in the DP-100 curriculum, through video lectures, demos, applications, and practice exams

Learn and master Azure Machine Learning, a service by Microsoft that enables anyone to build, deploy, and manage Data Science and Machine Learning solutions

Create a predictive service based on a model that you create, with a full end-to-end walkthrough

Design and prepare a machine learning solution

Explore data and train models

Prepare a model for deployment

Deploy and retrain a model

Requirements

Some familiarity with data science and machine learning concepts (like regressions, training and validation, hyperparameters) is helpful but not necessary

Some familiarity with Python language and data science libraries (Pandas, sklearn, etc.) is helpful but not necessary

Computer with web browser and internet

No Azure experience necessary, come hungry to learn!

Description

Do you want to quickly build, deploy, and scale Data Science and Machine Learning solutions, without knowing any in-depth code, worrying about containers / endpoints, or coding data pipelines?Do you want to learn and master Azure Machine Learning, an enterprise-grade service by Microsoft that gives you tools for the end-to-end machine learning lifecycle?Do you want to build, deploy, and manage high quality models faster and with confidence?Do you want to be certified from Microsoft, so that you can put it on your Resume/CV and showcase to potential employers that you know how to deploy Data Science solutions using Azure Machine Learning?Do you want to pass the Microsoft DP 100 on the first try, and want one single complete resource that has everything you need for the DP-100 certification?Then this is the course for you. Learn from over 15 hours of instructional content with video lectures, demos, real-life applications, and practice exams, with the only complete guide to everything you need to know to pass the DP-100 exam and receive your certification.This course gives you all the training you need to pass - with detailed lectures, demos, and practice questions for each of the 62 learning objectives within the DP100 curriculum. This course gives you the structure you need to succeed - we go through each learning objective in sequential order, so that you are never lost.This course is also for those students who want to learn Azure Machine Learning, and its underlying services. Along with the training required to pass the DP 100 certification, students master this tool.DP-100 Designing and Implementing a Data Science Solution on Azure and Azure Data Scientist Associate certification is also called DP-100, DP100, and DP 100 certifications, and so these are used interchangeably.What is the DP-100?The DP-100 is a certification exam offered by Microsoft, that enables you to receive the Azure Data Scientist Associate certification. The exam covers how to design, build, and deploy a Machine Learning solution using Azure Machine Learning. The certification enables you to proves to employers and clients that you can build and operationalize Machine Learning and Data Science solutions and understand the core capabilities of the Azure Machine Learning. The exam format varies, but is most often 40-60 questions within about 2 hours. DP-100 is also referred to as DP 100 or DP100.What is this course all about?The purpose of this course is to prepare you for the DP 100 Designing and Implementing a Data Science Solution on Azure exam, so that you can pass it on the first try. It offers you dedicated video lectures, walk through demos, real-life applications, and practice exams to maximize your chances of success. This course covers 100% of the 62 learning objectives in Microsoft's curriculum, and trains you to receive the certification on the first try.What does the DP-100 cover?The DP-100 covers how to use Azure Machine Learning to design and implement a Data Science and Machine Learning solution. Specifically, it covers how to design and prepare a Machine Learning solution, how to explore data and train models, how to prepare a model for deployment, and how to deploy and retrain a model. The curriculum covers everything about Azure Machine Learning Studio, in both the designer (no-code) workflow, the Automated ML workflow, and the coding (Python SDK, Notebooks) workflow.What are the prerequisites of taking the DP-100?Candidates should have subject matter expertise in applying data science and ML to implement and run ML workloads.What is Azure Machine Learning?Azure Machine Learning (or Azure ML for short) is a service from Microsoft to create, validate, and deploy Machine Learning and Data Science solutions. It covers everything you would need, from data preparation, to model training and validation, to endpoint model management, and monitoring / model management. It makes it easier for anyone to deploy Data Science and Machine Learning solutions, especially if you are not familiar with Data Science algorithms, container management, compute monitoring, etc. - it does that all for you. Azure Machine Learning lets Data Scientists focus on what matters most, and automates the rest. It gives the power of Data Science and Machine Learning to anyone.Why is Azure Machine Learning so important?Azure Machine Learning is Microsoft’s way to democratize Machine Learning and Data Science to the everyday user.Why should you get the DP-100 certification?The DP-100 certification from Microsoft is a recognized way to prove that you understand and can use Azure Machine Learning to build business critical machine learning models at scale. You can use the knowledge you learn in the DP-100 course to create impact in your organization, but deploying predictive services and endpoints. You can add it to your resume to significantly boost your chances of employment. Most employers even cover the cost of the training and exam because of the value that this certification provides. In certain countries, you can even receive ACE college credits.Why choose this course?Complete guide - this is the 100% complete, start to finish zero to hero training guide to passing the DP 100 exam. It includes lectures, demos, study guides, practice exams, and more. It is the only resource that you will ever need to ace the exam. It contains over 15 hours of instructional content!Full coverage - we go through Microsoft's curriculum one-by-one and cover all aspects of each of the 62 different learning objectives. This means no surprises on the exam, and it ensures that you are best prepared to pass the DP-100 exam on the first try.Structured to succeed - the course mirrors Microsoft's DP 100 curriculum exactly. Each of the 62 learning objectives has a combination of a PDF study guide, full video lectures, full video walk through demos, and application.Instructional and applicable - we not only go through important concepts, but also apply them as we are building our application so that we can solidify them. This is not only a walkthrough of the all the features and theoretical concepts, but a DP-100 course that actually builds applications with youPractice exams - this course contains practice exams with questions that exactly mirror the types of questions found on the DP-100 exam. Use them to validate your knowledge and find weaker areas where you need to review.Step by step - each learning objective within Microsoft's DP 100 curriculum is covered in order, step by step. This ensures that you never get lost in the course.Teacher response - if there's anything else you would like to learn, or if there's something you cannot figure out, I'm here for you! Look at the ways to reach out videoCommunity - when you enroll in this course, you join a DP100 community full of learners just like youMaster a new tool - Learn Azure Machine Learning, from basic no-code designer tools to fully customized code deployments using Python SDKCourse overview theoryThe course follows exactly according to DP100 curriculum, based on 62 learning objectives (LO) that Microsoft has defined. Everything in this course is made to maximize your chances of passing the exam. For each learning objective, the course offers a combination of guided lecture videos, walk-through demos, and application. We then end with practice exams.Course overviewIntroduction - learn about the DP 100 exam and how best to succeedEnvironment Setup - set up an Azure account so you can follow along, and review the curriculumLO1: Design and prepare a machine learning solution (20–25%)LO2: Explore data and train models (35–40%)LO3: Prepare a model for deployment (20–25%)LO4: Deploy and retrain a model (10–15%)Practice Exams - practice what you have learned to validate your knowledgeConclusion - take your exam, earn your certification, and next stepsIcons by Freepik / Flaticon. Music by Bensound.

Overview

Section 1: Introduction

Lecture 1 What is DP-100?

Lecture 2 What are the objectives of this course?

Lecture 3 Course roadmap

Lecture 4 Learning objectives

Lecture 5 Instructor overview

Lecture 6 Ways to reach out

Lecture 7 Keys to success

Lecture 8 Leave a rating

Lecture 9 Watch in 1080p

Section 2: Environment Setup

Lecture 10 Create an Azure account

Lecture 11 Cost management in Azure

Lecture 12 Reference material

Lecture 13 Resources and prerequisites

Lecture 14 Helpful advice from students

Section 3: LO1: Design and prepare a machine learning solution (20–25%)

Lecture 15 1-1-1 Determine the appropriate compute specifications

Lecture 16 1-1-2 Model deployment requirements

Lecture 17 1-1-3 Choice to development approach to build or train a model

Lecture 18 1-2-1 Create an Azure Machine Learning workspace

Lecture 19 1-2-1 Walkthrough of workspace

Lecture 20 1-2-1 Resources created by ML workspace

Lecture 21 1-2-1 How to access Azure ML tools

Lecture 22 1-2-1 Create a compute instance

Lecture 23 1-2-1 Run python SDK import statements

Lecture 24 1-2-1 Stopping compute instance

Lecture 25 1-3-1 Create Azure Data resources

Lecture 26 1-3-2 Create and register a datastore

Lecture 27 1-3-2 Example of transfering files to datastore

Lecture 28 1-3-3 Create a data asset

Lecture 29 1-3-3 Register a data asset through SDK

Lecture 30 1-3-3 Register and consume data assets through SDK

Section 4: LO2: Explore data and train models (35–40%)

Lecture 31 2-1-1 Load and transform data

Lecture 32 2-1-2 Analyze data using Azure Data Explorer 1

Lecture 33 2-1-2 Analyze data using Azure Data Explorer 2

Lecture 34 2-1-2 Use profile mechanics to explore data

Lecture 35 2-2-1 Create a training pipeline introduction

Lecture 36 2-2-2 Consume data assets into the designer

Lecture 37 2-2-3 Use data preparation components in designer

Lecture 38 2-2-3 Training model and scoring components in designer

Lecture 39 2-2-3 Evaluating trained model components in designer

Lecture 40 2-2-3 Evaluation results defined

Lecture 41 2-2-4 Context and use-case for custom code components

Lecture 42 2-2-4 Adding custom python code in custom components in designer

Lecture 43 2-3-1 Automated ML introduction

Lecture 44 2-3-1 Automated ML regression and tabular data example 1

Lecture 45 2-3-1 Automated ML regression and tabular data example 2

Lecture 46 2-3-1 Automated ML regression and tabular data example 3

Lecture 47 2-3-2 Automated ML natural language processing NLP example

Lecture 48 2-3-4 Training options in Automated ML, including preprocessing and algorithms

Lecture 49 2-4-1 Develop code using a compute instance

Lecture 50 2-4-2 Consume data in a notebook

Lecture 51 2-4-3 How to run an experiment

Lecture 52 2-4-4 2-4-5 Evaluate and train a model using Python SDK 1

Lecture 53 2-4-4 2-4-5 Evaluate and train a model using Python SDK 2

Lecture 54 2-4-4 2-4-5 Evaluate and train a model using Python SDK 3

Lecture 55 2-4-4 2-4-5 Run experiments and measure impact on evaluation metrics

Section 5: LO3: Prepare a model for deployment (20–25%)

Lecture 56 3-1-1 Introduction to model training scripts

Lecture 57 3-1-1 3-1-3 3-1-4 3-1-6 3-1-7 Run model training script end-to-end 1

Lecture 58 3-1-1 3-1-3 3-1-4 3-1-6 3-1-7 Run model training script end-to-end 2

Lecture 59 3-1-1 3-1-3 3-1-4 3-1-6 3-1-7 Run model training script end-to-end 3

Lecture 60 3-1-1 3-1-3 3-1-4 3-1-6 3-1-7 Run model training script end-to-end 4

Lecture 61 3-1-1 3-1-3 3-1-4 3-1-6 3-1-7 Run model training script end-to-end 5

Lecture 62 3-1-8 3-1-2 Configure compute and set up script parameters set up

Lecture 63 3-1-8 3-1-2 Using script parameters

Lecture 64 3-1-8 3-1-2 Cycling through script parameters

Lecture 65 3-1-8 3-1-2 Testing different script parameters

Lecture 66 3-1-8 3-1-2 Configure compute for a job run

Lecture 67 3-1-8 3-1-2 Adding compute to an environment

Lecture 68 3-1-8 3-1-2 Deleting a compute through Python SDK

Lecture 69 3-2-1 Introduction to pipelines

Lecture 70 3-2-1 Pipeline context

Lecture 71 3-2-1 Create a prepare data step in pipeline

Lecture 72 3-2-1 Create a train model step in pipeline

Lecture 73 3-2-1 Fix errors in pipeline

Lecture 74 3-2-1 Create a pipeline run script

Lecture 75 3-2-2 Pass data between steps in pipeline

Lecture 76 3-2-3 Run the pipeline

Lecture 77 3-2-3 Other ways to run the pipeline

Lecture 78 3-2-3 Publishing the endpoint

Lecture 79 3-2-3 Create a pipeline endpoint

Lecture 80 3-2-3 Call an endpoint 1

Lecture 81 3-2-3 Call an endpoint 2

Lecture 82 3-2-4 Monitor pipeline runs

Section 6: LO4: Deploy and retrain a model (10–15%)

Lecture 83 4-1-1 4-1-3 4-1-5 Introduction to deploying model

Lecture 84 4-1-1 4-1-3 4-1-5 Create a model to be deployed

Lecture 85 4-1-1 4-1-3 4-1-5 Configure model for a real-time deployment

Lecture 86 4-1-1 4-1-3 4-1-5 Removing the dependent variable

Lecture 87 4-1-1 4-1-3 4-1-5 Deploy a model to a real-time endpoint

Lecture 88 4-1-1 4-1-3 4-1-5 Test a real-time deployed service

Lecture 89 4-1-1 4-1-3 4-1-5 Consume the deployed model in endpoint

Lecture 90 4-1-1 4-1-3 4-1-5 Make modifications to deployed model

Lecture 91 4-1-1 4-1-3 4-1-5 Redeploy a model

Section 7: Practice Exams

Section 8: Conclusion

Lecture 92 Congratulations

Lecture 93 Conclusion and next steps

Lecture 94 Ways to reach out

Lecture 95 Certificate

Section 9: Bonus

Lecture 96 Bonus

Business Analysts who want to build, test, and deploy models quickly, especially if they want to create Data Science solutions and predictive services,Students who want to pass the DP-100 exam and receive the Azure Data Scientist Associate certification,Individuals who want to receive a formal certificate from Microsoft for their progress and achievement (useful for moving upwards and getting hired),Users who want to create and deploy Data Science and Machine Learning solutions with no-code,Data Scientists who want a more streamlined approach to creating, deploying, and managing Machine Learning solutions and services,Data Scientists and Machine Learning Engineers who want to focus on what matters most, and want to automate the rest (algorithm and hyperparameter tuning, endpoint containerization),Anyone who wants to learn Azure Machine Learning, a tool for building ML model services, from the most basic to the most advanced,Students who want to make a career in Data Science and Machine Learning