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Offered By: IBMSkillsNetwork

Linear Regression with PyTorch

Linear regression is one of the most used technique for prediction. This course will give you a comprehensive understanding of linear regression modelling using the PyTorch framework. Equipped with these skills, you will be prepared to tackle real-world regression problems and utilize PyTorch effectively for predictive analysis tasks. It focuses specifically on the implementation and practical application of linear regression algorithms for predictive analysis. Note, this course is a part of a PyTorch Learning Path, find more in the Prerequisites Section.

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Course

Artificial Intelligence

413 Enrolled
4.7
(28 Reviews)

At a Glance

Linear regression is one of the most used technique for prediction. This course will give you a comprehensive understanding of linear regression modelling using the PyTorch framework. Equipped with these skills, you will be prepared to tackle real-world regression problems and utilize PyTorch effectively for predictive analysis tasks. It focuses specifically on the implementation and practical application of linear regression algorithms for predictive analysis. Note, this course is a part of a PyTorch Learning Path, find more in the Prerequisites Section.

Throughout the course, students will learn the fundamental concepts and techniques of linear regression. They will gain proficiency in constructing and training linear regression models using PyTorch, utilizing both single and multiple independent variables to predict continuous target variables. Using PyTorch, students will implement linear regression models and train them using gradient descent optimization algorithms. They will gain hands-on experience in adjusting model parameters, evaluating model performance, and making predictions on unseen data.

Course Syllabus

In this course we will learn about:

Module 1:
  1. Linear Classifier and Logistic Regression
  2. Linear Regression Training
  3. Gradient Descent and Cost
  4. PyTorch Slope
  5. Linear Regression Training

Module 2:
  1. Stochastic Gradient Descent and the Data Loader
  2. Mini-Batch Descent
  3. Optimization in PyTorch
  4. Training, Validation and Test Split
  5. Multiple Linear Regression Prediction
  6.  Multiple Output Linear Regression

Prerequisites


Note: this course is a part of PyTorch Learning Path and the following is required 
  1. Completion of PyTorch: Tensor, Dataset and Data Augmentation course
or 

Good understanding of PyTorch Tensors and DataSets

Skills Prior to Taking this Course

  • Basic knowledge of Python programming language.
  • Basic knowledge of PyTorch Framework

Estimated Effort

7 Hours

Level

Beginner

Skills You Will Learn

Artificial Intelligence, General Statistics, PyTorch

Language

English

Course Code

AI0116EN

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This Course is part of the following Learning Paths

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