Linear Regression Models: Introduction to Logistic Regression

Machine Learning    |    Intermediate
  • 11 videos | 57m 50s
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 13 users Rating 4.3 of 13 users (13)
Logistic regression is a technique used to estimate the probability of an outcome for machine learning solutions. In this 10-video course, learners discover the concepts and explore how logistic regression is used to predict categorical outcomes. Key concepts covered here include the qualities of a logistic regression S-curve and the kind of data it can model; learning how a logistic regression can be used to perform classification tasks; and how to compare logistic regression with linear regression. Next, you will learn how neural networks can be used to perform a logistic regression; how to prepare a data set to build, train, and evaluate a logistic regression model in Scikit Learn; and how to use a logistic regression model to perform a classification task and evaluate the performance of the model. Learners observe how to prepare a data set to build, train, and evaluate a Keras sequential model, and how to build, train, and validate Keras models by defining various components, including activation functions, optimizers and the loss function.

WHAT YOU WILL LEARN

  • Identify the types of problems which can be solved by logistic regression
    Describe the qualities of a logistic regression s-curve and understand the kind of data it can model
    Recognize how a logistic regression can be used to perform classification tasks
    Compare logistic regression with linear regression
    Recall how neural networks can be used to perform a logistic regression
  • Prepare a dataset to build, train and evaluate a logistic regression model in scikit learn
    Use a logistic regression model to perform a classification task and evaluate the performance of the model
    Prepare a dataset to build, train and evaluate a keras sequential model
    Build, train and validate the keras model by defining various components including the activation functions, optimizers and the loss function
    Employ key classification techniques in logistical regression

IN THIS COURSE

  • 2m 11s
  • 6m 57s
    In this video, find out how to identify the types of problems which can be solved by logistic regression. FREE ACCESS
  • Locked
    3.  The Logistic Regression Curve
    5m 7s
    Upon completion of this video, you will be able to describe the qualities of a logistic regression S-curve and understand the data it can model. FREE ACCESS
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    4.  Logistic Regression and Classification
    7m 38s
    Upon completion of this video, you will be able to recognize how to use a logistic regression to perform classification tasks. FREE ACCESS
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    5.  Logistic Regression vs. Linear Regression
    3m 29s
    To compare logistic regression with linear regression, find out how to do so. FREE ACCESS
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    6.  Logistic Regression in Keras
    7m 35s
    Upon completion of this video, you will be able to recall how neural networks can be used to perform a logistic regression. FREE ACCESS
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    7.  Preparing Data for Logistic Regression
    7m 46s
    In this video, you will learn how to prepare a dataset, build, train, and evaluate a logistic regression model in Scikit Learn. FREE ACCESS
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    8.  Classification using a Logistic Regression Model
    2m 31s
    In this video, you will use a logistic regression model to perform a classification task and evaluate the performance of the model. FREE ACCESS
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    9.  Preparing Data for a Neural Network
    4m 9s
    Learn how to prepare a dataset to build, train, and evaluate a Keras sequential model. FREE ACCESS
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    10.  Building and Evaluating the Keras Classifier
    6m
    During this video, you will learn how to build, train, and validate the Keras model by defining various components including the activation functions, optimizers, and the loss function. FREE ACCESS
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    11.  Exercise: An Introduction to Logistic Regression
    4m 27s
    In this video, you will use key classification techniques in logistical regression. FREE ACCESS

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