Microsoft DP-100 Online Training Course

In the exam preparation course, students can understand the machine learning flow of a professional Azure AI, use data to experiment, manage, optimize and train predictive models. Students can obtain the globally recognized Microsoft DP-100 Certificate exam upon course completion. 

*All courses must be completed within one year 

  1. 全課程導師費: HKD 350 x 20小時
    = HKD 7,000
  2. 訂閱平台或教材費: HKD 80 (一年)
  3. 考試券: / (學生自行安排考試)
  4. 硬件: /
  5.  

HKD 7,080
(USD 932)

What you will learn

Are you suitable for this course?

Course Content

Introduce the basic concepts of machine learning 

  • Learn the input and output of machine learning 
  • Optimise a model using cost function 

 

Explore data analysis tasks and real world data 

  • Explore data with Numpy and Pandas 
  • Visualise Data using Matplotlib 
  • Examine Real world data issues 

Understand the procedures of completing regression models 

  • Experiment with different model 
  • Improve models with hyperparameters 
  • Optimisation of the model

Understand the procedures of completing classification models 

  • Train and evaluate a classification model 
  • Perform classification with alternative metrics 
  • Train and evaluate multiclass classification model 

Understand the procedures of completing clustering models 

  • Train and evaluate a clustering model 
  • Evaluate different types of clustering 
  • Train and evaluate advanced clustering models  

Explore the concept of deep learning 

  • Learn the concept of deep neural network  
  • Train a convolutional neural network 
  • Experience transfer learning 

Learn the concepts of machine learning through auto ML and Designer 

  • Introduction to auto ML and Designer  
  • Train, evaluate and deploy a regression model using designer 
  • Train, evaluate and deploy a classification model using designer 
  • Train, evaluate and deploy a clustering model using designer 

AML: Introduction of Azure Machine Learning 

  • Understand the idea of  Azure Machine Learning SDK 
  • Train a machine learning model with AML  
  • Work with data in AML 
  • Work with compute in AML 

AML: Pipeline training 

  • Orchestrate machine learning with pipeline  
  • Deploy real-time machine learning services with AML 
  • Deploy batch inference pipelines with AML 

AML: Tuning the model 

  • Tune hyperparameters with AML 
  • Automate machine learning model selection with AML 
  • Explain machine learning models with AML 
  • Detect and mitigate unfairness in models with AML 

AML: Monitor and secure the model 

  • Monitor models with AML 
  • Monitor data drifts with AML 
  • Explore security concepts in AML 
  • Explore differential privacy 

AD: Tutorial and preparation for using Azure Databricks 

  • Get started with Azure Databricks 
  • Understand Azure Databricks Notebooks 
  • Work with Notebooks 

AD:  Using Spark and configure data in AD 

  • Spark architecture fundamentals 
  • Read and write data in Azure Databricks 
  • Work with DataFrames in Azure Databricks 
  • Work with user-defined function 

AD: Work with other functions of AD for data processing 

  • Build and query a Delta Lake 
  • Perform machine learning with AD and Spark 
  • Work with MLflow in Databricks 

AD: 

  • Tune hyperparameters with AD for model selection 
  • Work with MLflow in Databricks 
  • Distributed deep learning with Horovod and AD 
  • Use AML to deploy serving models

  • 120mins – Mock Exam 
  • 240mins – Explanation 

Introduce the basic concepts of machine learning 

  • Learn the input and output of machine learning 
  • Optimise a model using cost function 

 

Explore data analysis tasks and real world data 

  • Explore data with Numpy and Pandas 
  • Visualise Data using Matplotlib 
  • Examine Real world data issues 

Understand the procedures of completing regression models 

  • Experiment with different model 
  • Improve models with hyperparameters 
  • Optimisation of the model

Understand the procedures of completing classification models 

  • Train and evaluate a classification model 
  • Perform classification with alternative metrics 
  • Train and evaluate multiclass classification model 

Understand the procedures of completing clustering models 

  • Train and evaluate a clustering model 
  • Evaluate different types of clustering 
  • Train and evaluate advanced clustering models  

Explore the concept of deep learning 

  • Learn the concept of deep neural network  
  • Train a convolutional neural network 
  • Experience transfer learning 

Learn the concepts of machine learning through auto ML and Designer 

  • Introduction to auto ML and Designer  
  • Train, evaluate and deploy a regression model using designer 
  • Train, evaluate and deploy a classification model using designer 
  • Train, evaluate and deploy a clustering model using designer 

AML: Introduction of Azure Machine Learning 

  • Understand the idea of  Azure Machine Learning SDK 
  • Train a machine learning model with AML  
  • Work with data in AML 
  • Work with compute in AML 

AML: Pipeline training 

  • Orchestrate machine learning with pipeline  
  • Deploy real-time machine learning services with AML 
  • Deploy batch inference pipelines with AML 

AML: Tuning the model 

  • Tune hyperparameters with AML 
  • Automate machine learning model selection with AML 
  • Explain machine learning models with AML 
  • Detect and mitigate unfairness in models with AML 

AML: Monitor and secure the model 

  • Monitor models with AML 
  • Monitor data drifts with AML 
  • Explore security concepts in AML 
  • Explore differential privacy 

AD: Tutorial and preparation for using Azure Databricks 

  • Get started with Azure Databricks 
  • Understand Azure Databricks Notebooks 
  • Work with Notebooks 

AD:  Using Spark and configure data in AD 

  • Spark architecture fundamentals 
  • Read and write data in Azure Databricks 
  • Work with DataFrames in Azure Databricks 
  • Work with user-defined function 

AD: Work with other functions of AD for data processing 

  • Build and query a Delta Lake 
  • Perform machine learning with AD and Spark 
  • Work with MLflow in Databricks 

AD: 

  • Tune hyperparameters with AD for model selection 
  • Work with MLflow in Databricks 
  • Distributed deep learning with Horovod and AD 
  • Use AML to deploy serving models

  • 120mins – Mock Exam 
  • 240mins – Explanation 

Feedback

no feedback

FAQ

Students need to login both Microsoft Teams and Una Platform for learning and coding, but it is unnecessary to use the same device.

  1. Microsoft Teams: Please download Microsoft Teams here and find out the detail of device requirements.
  2. Una Platform: Student can login Una with an internet-accessible computer, laptop or iPad, using Google Chrome or Microsoft Edge up-to-date browsers.

Important Notes for Login Una:

  1. Smartphone is not supported
  2. For iPad users, it requires iPadOS 14.5 or above
  3. Login with computer or laptop, using normal mode on the browser (NOT Incognito Mode), can enjoy a better user experience
  1. When we receive your registration, we will send you Una Platform login instruction.
  2. Once you receive login instruction, before the class, please visit https://app.una.study/ and see if you can login Una successfully. Otherwise, please change your device or update your browser prior class to ensure we can start the lesson on time and smoothly.

Microsoft DP-100 Online Training Course

HKD 7,080 (USD 932)