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Linear Regression with PyTorch

Beginnercourse

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.

4.5 (143 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 1.16K

Skills You Will Learn

  • Artificial Intelligence, PyTorch, General Statistics

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 7 hours

Platform

  • SkillsNetwork

Last Update

  • February 22, 2025
About this course
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

Instructors

Artem Arutyunov

Data Scientist

Hey, Artem here! I am excited about answering new challenges with data science, machine learning and especially Reinforcement Learning. Love helping people to learn, and learn myself. Studying Math and Stats at University of Toronto, hit me up if you are from there as well.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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