COURSE 2: MICROSOFT AZURE MACHINE LEARNING FOR DATA SCIENTIST

Module 1: Use Automated Machine Learning In Azure Machine Learning

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TABLE OF CONTENT

INTRODUCTION – Use Automated Machine Learning In Azure Machine Learning

Training a machine learning model is an iterative process that demands significant time and computing resources. Automated machine learning can streamline and simplify this process. In this module, you will learn to identify various types of machine learning models and utilize the automated machine learning capabilities of Azure Machine Learning to train and deploy a predictive model.

PRACTICE QUIZ: KNOWLEDGE CHECK

1. True or False?

Machine learning is a technique that uses statistics to create a model that can predict unknown values.

  • True 
  • False (CORRECT)

Correct: Machine learning is a technique that uses mathematics and statistics to create a model that can predict unknown values.

2. What model is best suited for predicting categories or classes?

  • Classification (CORRECT)
  • Regression 
  • Time series forecasting 

Correct: Classification is best suited for predicting categories or classes.

3. True or False?

The “Predicted vs. True” chart shows a diagonal trend in which the predicted value correlates closely to the true value.

  • True (CORRECT)
  • False

Correct: The “Predicted vs. True” chart should show a diagonal trend in which the predicted value correlates closely to the true value.

4. If you want to automatically pre-process the features before training, what setting should you use?

  • Enable featurization (CORRECT)
  • Explain best model
  • Training job time

Correct: Enable featurization causes Azure Machine Learning to automatically pre-process the features before training.

5. In a residual histogram, what do residuals represent?

  • Variance between predicted and false values that can be explained by the model
  • Variance between predicted and false values that cannot be explained by the model
  • Variance between predicted and true values that cannot be explained by the model (CORRECT)

Correct: Residuals represent variance between predicted and true values that can’t be explained by the model.

QUIZ: TEST PREP

1. A hospital wants to categorize patients that are pregnant as low-risk or high-risk regarding complications based on data like patient age and known medical conditions. What kind of machine learning model should the hospital use?

  • Classification (CORRECT)
  • Regression
  • Time series forecasting

Correct: To predict a category, or class, a classification model can be used.

2. Which of the following are machine learning models?

  • Polarization
  • Regression (CORRECT)
  • Time series forecasting (CORRECT)

Correct: Time series forecasting is a machine learning model.

Correct: Time series forecasting is a machine learning model.

3. A meteorological institute wants to predict, based on data from the past, how much it will rain next Sunday. What machine learning model is the best fit for this case?

  • Time series forecasting (CORRECT)
  • Regression
  • Classification

Correct: Time series forecasting enables predictions of numeric values at a future point in time.

4. A toy company wants to predict the daily demand in order to assure that they have the necessary stock to honour all orders. What machine learning model can be used in this case?

  • Classification
  • Clustering 
  • Regression (CORRECT)

Correct: Regression is a supervised machine learning technique used to predict numeric values.

5. True or False?

Azure Machine Learning includes an automated machine learning capability that leverages the scalability of cloud compute to automatically try multiple pre-processing techniques and model-training algorithms in parallel to find the best performing supervised machine learning model for your data.

  • True (CORRECT)
  • False

Correct: Azure Machine Learning includes an automated machine learning capability that leverages the scalability of cloud compute to automatically try multiple pre-processing techniques and model-training algorithms in parallel to find the best performing supervised machine learning model for your data.

6. True or False?

A bike rental company can use historic data to train a model that predicts daily rental demand in order to make sure sufficient staff and cycles are available.

  • True (CORRECT)
  • False

Correct: A regression model can fulfil this task.

7. What setting should you configure if you want to end the experiment if the model achieves a certain score or less on normalized root mean squared error metric?

  • Metric score threshold (CORRECT)
  • Blocked algorithms 
  • Training compute target

Correct: This metric causes the experiment to end if a model achieves a certain score (or less) on normalized root mean squared error.

CONCLUSION – Use Automated Machine Learning In Azure Machine Learning

In conclusion, training a machine learning model is a time-intensive and resource-demanding iterative process. However, automated machine learning significantly simplifies this task. In this module, you have learned to identify different types of machine learning models and harness the automated machine learning capabilities of Azure Machine Learning to efficiently train and deploy predictive models. By mastering these skills, you are now equipped to streamline the model development process and enhance your machine learning projects.

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