Machine Learning with BigQuery ML: Building Classification Models

BigQuery ML 2023    |    Intermediate
  • 13 videos | 1h 47m 1s
  • Includes Assessment
  • Earns a Badge
Rating 3.0 of 1 users Rating 3.0 of 1 users (1)
Predictive models that output discrete classes or categories are classification models. Classification is widely used in the real world for use cases such as sentiment analysis of text and identifying objects in images. In this course, you will review how classification models can be used to categorize or classify input records. You will learn how metrics such as accuracy, precision, and recall can be used to evaluate classification models and the conditions under which you would choose to use precision and recall over accuracy for model evaluation. Next, you will use the BigQuery command-line tool bq to create a BigQuery dataset and table and load data into that table. You will see how you can run queries and explore your data, all using the command line. You will use Looker Studio for data visualization and DataPrep to clean and prepare your classification data. Finally, you will train a binary classification model and a multi-class classification model. You will improve the model's performance by balancing the records in the different categories and by using hyperparameter tuning to find the best model for your data.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Provide an overview of how classification models work and their use cases
    Execute commands in cloud shell using bq
    Clean and prepare data in dataprep
    Split and visualize data in looker studio
    Run a classification model and adjust it
    Balance skewed classes in data
  • Remove columns with low predictive power
    Extract numeric values from text columns
    Impute missing values and clean outliers
    Compare logistic regression and boosted tree models
    Perform hyperparameter tuning on a random forest
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 42s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 6m 52s
    After completing this video, you will be able to provide an overview of how classification models work and their use cases. FREE ACCESS
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    3.  Loading and Exploring Data using the bq Command-line Utility
    8m 39s
    Learn how to execute commands in Cloud Shell using bq. FREE ACCESS
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    4.  Cleaning and Preparing Data
    12m 11s
    Find out how to clean and prepare data in DataPrep. FREE ACCESS
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    5.  Running the DataPrep Pipeline and Data Exploration
    7m 58s
    In this video, find out how to split and visualize data in Looker Studio. FREE ACCESS
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    6.  Training and Evaluating a Logistic Regression Model
    10m 30s
    In this video, you will learn how to run a classification model and adjust it. FREE ACCESS
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    7.  Balancing Skewed Data in Logistic Regression Models
    9m 21s
    Learn how to balance skewed classes in data. FREE ACCESS
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    8.  Loading Data and Deleting Unwanted Columns
    7m 47s
    Find out how to remove columns with low predictive power. FREE ACCESS
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    9.  Data Cleaning and Imputing Missing Values
    10m 31s
    In this video, find out how to extract numeric values from text columns. FREE ACCESS
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    10.  Extracting Data and Outlier Treatment
    10m 34s
    Learn how to impute missing values and clean outliers. FREE ACCESS
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    11.  Training Models for Multi-class Classification
    7m 17s
    Learn how to compare logistic regression and boosted tree models. FREE ACCESS
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    12.  Performing Hyperparameter Tuning of a Random Forest Model
    11m 30s
    Find out how to perform hyperparameter tuning on a random forest. FREE ACCESS
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    13.  Course Summary
    2m 11s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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