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Course Description

Linear models are a class of supervised learning models that are represented by an equation and use a linear combination of features and weights to compute the label of an unlabeled example. Linear models are simple to implement, fast to train, and relatively low in complexity.

In this course, you will explore several linear models, including logistic regression, one of the most powerful linear models used in classification. Logistic regression is used to predict the probability of an outcome. While the focus of the unit will be on logistic regression, you will also be introduced to a common linear model used to solve regression problems: linear regression. You will delve into important concepts specific to the training of linear models, including the optimization algorithm, gradient descent, and the loss function evaluation tool. You will be given the opportunity to implement a logistic regression model from scratch using NumPy, and you will see a demonstration of how a linear regression model can be used to solve real-world regression problems, applying your experience to relevant scenarios.

You are required to have completed the following courses or have equivalent experience before taking this course:

  • Machine Learning Foundations
  • Managing Data in Machine Learning
  • Training Common Machine Learning Models

Faculty Author

Brian D'Alessandro

Benefits to the Learner

  • Analyze the mechanics of logistic regression
  • Understand the purpose of using gradient descent and loss functions
  • Explore common hyperparameters for logistic regression
  • Define the core math concepts required to solve common machine learning problems
  • Use NumPy to perform vector and matrix operations
  • Explore how linear regression works to solve real-world regression problems

Target Audience

  • Data scientists and data analysts
  • Programmers, developers, and software engineers
  • Statisticians
  • Product managers
  • Entrepreneurs
  • Working professionals seeking to upskill or career change

Applies Towards the Following Certificates

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Enroll Now - Select a section to enroll in
Type
2 week
Dates
May 22, 2024 to Jun 04, 2024
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
Type
2 week
Dates
Aug 14, 2024 to Aug 27, 2024
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
Type
2 week
Dates
Nov 06, 2024 to Nov 19, 2024
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
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