Using BigML: Building Supervised Learning Models

Machine Learning    |    Intermediate
  • 14 videos | 1h 30m
  • Includes Assessment
  • Earns a Badge
Rating 4.7 of 6 users Rating 4.7 of 6 users (6)
The versatility of BigML allows you to build supervised learning models without much complexity. In this course, you'll practice constructing a selection of supervised learning models using BigML. You'll start by building an ensemble of decision trees to perform binary classification. Next, you'll build a linear regression model to predict the values of homes in a particular region. You'll then train and evaluate a logistic regression model to illustrate how it can be used to solve similar problems to those solved using ensemble methods. Another BigML capability you'll explore is building a time series plot to make various forecasts. In each demonstration, you'll delve into some optional configurations for the model being trained. Lastly, you'll use the OptiML feature to find the optimal model for your data.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Use bigml to build an ensemble of decision trees to solve a classification problem
    Recognize the properties of ensemble models configured in bigml
    Compare the performance of a small ensemble with a larger one
    Prepare a dataset for use in a linear regression model
    Build a linear regression model and identify the relationships it uncovers between the input variables and the output
    Recognize the different factors involved in evaluating a linear regression model
  • Describe the process of preparing a dataset for logistic regression
    Train a logistic regression model to predict an output based on probability of occurrence
    Check the performance of a logistic regression model using a test data
    Create a time series model using several years' worth of data
    Apply past data to make future forecasts using a time series model
    Apply a brute-force approach to find the optimal model for your dataset
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 38s
  • 8m 56s
    In this video, you will use BigML to build an ensemble of decision trees to solve a classification problem. FREE ACCESS
  • Locked
    3.  Configuring an Ensemble
    9m 20s
    After completing this video, you will be able to recognize the properties of ensemble models configured in BigML. FREE ACCESS
  • Locked
    4.  Creating a Large Ensemble
    3m 47s
    In this video, find out how to compare the performance of a small ensemble with a larger one. FREE ACCESS
  • Locked
    5.  Building a Dataset for Linear Regression
    6m 42s
    To prepare a dataset for use in a linear regression model, find out how to: - Choose the right variables - Handle missing data - Transform variables, if needed - Create interaction terms, if needed - Choose the right type of model FREE ACCESS
  • Locked
    6.  Training and Analyzing a Linear Regression Model
    8m 8s
    During this video, you will learn how to build a linear regression model and identify the relationships it uncovers between the input variables and the output variable. FREE ACCESS
  • Locked
    7.  Using and Evaluating a Linear Regression Model
    5m 33s
    Upon completion of this video, you will be able to recognize the different factors involved in evaluating a linear regression model. FREE ACCESS
  • Locked
    8.  Preparing a Dataset for Logistic Regression
    8m 56s
    After completing this video, you will be able to describe the process of preparing a dataset for logistic regression. FREE ACCESS
  • Locked
    9.  Building a Logistic Regression Model
    8m 2s
    Learn how to train a logistic regression model to predict an output based on the probability of occurrence. FREE ACCESS
  • Locked
    10.  Evaluating a Logistic Regression Model
    4m 3s
    During this video, you will learn how to check the performance of a logistic regression model using test data. FREE ACCESS
  • Locked
    11.  Generating a Time Series
    8m 10s
    In this video, you will create a time series model using data from several years. FREE ACCESS
  • Locked
    12.  Forecasting with and Evaluating a Time Series
    5m 39s
    During this video, you will learn how to use past data to make future forecasts using a time series model. FREE ACCESS
  • Locked
    13.  Using OptiML to Find the Best Model
    8m 26s
    In this video, find out how to apply a brute-force approach to find the optimal model for your data set. FREE ACCESS
  • Locked
    14.  Course Summary
    1m 38s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Course Machine Learning with BigQuery ML: Building Regression Models
Rating 4.3 of 7 users Rating 4.3 of 7 users (7)
Course Fundamentals of AI & ML: Metrics & Evaluation
Rating 5.0 of 2 users Rating 5.0 of 2 users (2)
Book Delivering Machine Learning Projects: From Design to Deployment

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Course AWS Certified Machine Learning: Problem Framing & Algorithm Selection
Rating 4.6 of 20 users Rating 4.6 of 20 users (20)
Course The Math Behind Decision Trees: An Exploration of Decision Trees
Rating 4.5 of 2 users Rating 4.5 of 2 users (2)
Course SQL Programming with MariaDB: Using Joins, Triggers, & Stored Procedures
Rating 4.7 of 27 users Rating 4.7 of 27 users (27)