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Classification with PyTorch

BeginnerCourse

Designed for students and enthusiasts, this course equips you with the knowledge and practical skills to build powerful and accurate classification models using PyTorch. It offers a hands-on learning experience, allowing you to apply your knowledge through coding exercises and lessons so by the end of the course, you will possess the skills to build, train, and evaluate classification models using PyTorch. "Classification with PyTorch" is a part of a PyTorch Learning Path, check Prerequisites.

4.6 (86 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 726

Skills You Will Learn

  • Artificial Intelligence, PyTorch, Python

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 4 hours

Platform

  • SkillsNetwork

Last Update

  • March 13, 2025
About this Course

Throughout the course, students will learn how to construct linear models and implement logistic regression algorithms using PyTorch. They will gain proficiency in making predictions using logistic regression models and understanding the underlying probabilistic interpretation. Students will also delve into Bernoulli distribution maximum likelihood estimation and logistic regression cross-entropy, enabling them to effectively estimate model parameters and optimize them for classification tasks. Furthermore, the course covers the application of the softmax function for multiclass classification, providing students with the necessary knowledge to perform accurate and reliable multiclass classification using PyTorch.

Syllabus 

In this course we will learn about:
  1. Linear Classifier and Logistic Regression
  2. Logistic Regression Prediction
  3. Bernoulli Distribution Maximum Likelihood Estimation
  4. Logistic Regression Cross Entropy
  5. Softmax Function
  6. Softmax PyTorch

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
  2. Completion of Linear Regression with PyTorch course

or 

Good understanding of PyTorch Tensors, DataSets and Linear Regression

Skills Prior to Taking this Course

  • Basic knowledge of Python programming language.
  • Basic knowledge of PyTorch Framework
  • Familiarity with fundamental concepts of machine learning and deep learning is beneficial but not mandatory.

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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Roodra Kanwar

Data Scientist at IBM

I am a data scientist by day, superhero by night. Psych! I wish I was that cool. Only the former part is true which is still pretty cool! I believe in constant learning and it is an essential part of being a productive data enthusiast. I am also pursuing my masters in computer science from Simon Fraser University specializing in Big Data. Moreover, knowledge is transfer learning (pun intended!) and what I have gained, I plan on reflecting it back to the data community.

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