DP-100 Azure Data Scientist Associate Complete Exam Guide

سرفصل های دوره

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

1 - Introduction

1 - What is DP100

2 - What are the objectives of this course

3 - Course roadmap

4 - DP-100-Curriculum.pdf

4 - Learning objectives

5 - Instructor overview

6 - Ways to reach out

7 - Keys to success

8 - Leave a rating

9 - Watch in 1080p

2 - Environment Setup

10 - Create an Azure account

11 - Cost management in Azure

12 - ReferenceMaterial.zip

12 - Reference material

13 - DP-100-Resources-PreReqs.pdf

13 - Resources and prerequisites

14 - Helpful advice from students

3 - LO1 Design and prepare a machine learning solution 2025

15 - 111 Determine the appropriate compute specifications

16 - 112 Model deployment requirements

17 - 113 Choice to development approach to build or train a model

18 - 121 Create an Azure Machine Learning workspace

19 - 121 Walkthrough of workspace

20 - 121 Resources created by ML workspace

21 - 121 How to access Azure ML tools

22 - 121 Create a compute instance

23 - 121 Run python SDK import statements

24 - 121 Stopping compute instance

25 - 131 Create Azure Data resources

26 - 132 Create and register a datastore

27 - 132 Example of transfering files to datastore

28 - 133 Create a data asset

29 - 133 Register a data asset through SDK

30 - 133 Register and consume data assets through SDK

4 - LO2 Explore data and train models 3540

31 - 211 Load and transform data

32 - 212 Analyze data using Azure Data Explorer 1

33 - 212 Analyze data using Azure Data Explorer 2

34 - 212 Use profile mechanics to explore data

35 - 221 Create a training pipeline introduction

36 - 222 Consume data assets into the designer

37 - 223 Use data preparation components in designer

38 - 223 Training model and scoring components in designer

39 - 223 Evaluating trained model components in designer

40 - 223 Evaluation results defined

41 - 224 Context and usecase for custom code components

42 - 224 Adding custom python code in custom components in designer

43 - 231 Automated ML introduction

44 - 231 Automated ML regression and tabular data example 1

45 - 231 Automated ML regression and tabular data example 2

46 - 231 Automated ML regression and tabular data example 3

47 - 232 Automated ML natural language processing NLP example

48 - 234 Training options in Automated ML including preprocessing and algorithms

49 - 241 Develop code using a compute instance

50 - 242 Consume data in a notebook

51 - 243 How to run an experiment

52 - 244 245 Evaluate and train a model using Python SDK 1

53 - 244 245 Evaluate and train a model using Python SDK 2

54 - 244 245 Evaluate and train a model using Python SDK 3

55 - 244 245 Run experiments and measure impact on evaluation metrics

5 - LO3 Prepare a model for deployment 2025

56 - 311 Introduction to model training scripts

57 - 311 313 314 316 317 Run model training script endtoend 1

58 - 311 313 314 316 317 Run model training script endtoend 2

59 - 311 313 314 316 317 Run model training script endtoend 3

60 - 311 313 314 316 317 Run model training script endtoend 4

61 - 311 313 314 316 317 Run model training script endtoend 5

62 - 318 312 Configure compute and set up script parameters set up

63 - 318 312 Using script parameters

64 - 318 312 Cycling through script parameters

65 - 318 312 Testing different script parameters

66 - 318 312 Configure compute for a job run

67 - 318 312 Adding compute to an environment

68 - 318 312 Deleting a compute through Python SDK

69 - 321 Introduction to pipelines

70 - 321 Pipeline context

71 - 321 Create a prepare data step in pipeline

72 - 321 Create a train model step in pipeline

73 - 321 Fix errors in pipeline

74 - 321 Create a pipeline run script

75 - 322 Pass data between steps in pipeline

76 - 323 Run the pipeline

77 - 323 Other ways to run the pipeline

78 - 323 Publishing the endpoint

79 - 323 Create a pipeline endpoint

80 - 323 Call an endpoint 1

81 - 323 Call an endpoint 2

82 - 324 Monitor pipeline runs

6 - LO4 Deploy and retrain a model 1015

83 - 411 413 415 Introduction to deploying model

84 - 411 413 415 Create a model to be deployed

85 - 411 413 415 Configure model for a realtime deployment

86 - 411 413 415 Removing the dependent variable

87 - 411 413 415 Deploy a model to a realtime endpoint

88 - 411 413 415 Test a realtime deployed service

89 - 411 413 415 Consume the deployed model in endpoint

90 - 411 413 415 Make modifications to deployed model

91 - 411 413 415 Redeploy a model

7 - Practice Exams

8 - Conclusion

92 - Congratulations

93 - Conclusion and next steps

94 - Ways to reach out

95 - Certificate

9 - Bonus

96 - Bonus

139,000 تومان

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