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PyTorch Basics for Machine Learning

IntermediateCourse

This course is the first part in a two part course and will teach you the fundamentals of PyTorch. In this course you will implement classic machine learning algorithms, focusing on how PyTorch creates and optimizes models. You will quickly iterate through different aspects of PyTorch giving you strong foundations and all the prerequisites you need before you build deep learning models.

Language

  • English

Topic

  • Machine Learning

Skills You Will Learn

  • PyTorch, Deep Learning

Offered By

  • IBM

Estimated Effort

  • 20 hours

Platform

  • edX

Last Update

  • February 5, 2025
About this Course

What you'll learn

  • Build a Machine learning pipeline in PyTorch
  • Train Models in PyTorch.
  • Load large datasets
  • Train machine learning applications with PyTorch
  • Have the prerequisite Knowledge to apply to deep learning and
    how to incorporate and Python libraries such as Numpy and Pandas with PyTorch

Syllabus


Module 1
  • Tensors 1D
  • Two-Dimensional Tensors
  • Derivatives In PyTorch
  • Dataset
Module 2
  • Prediction Linear Regression
  • Training Linear Regression
  • Loss
  • Gradient Descent
  • Cost
  • Training PyTorch
Module 3
  • Gradient Descent
  • Mini-Batch Gradient Descent
  • Optimization in PyTorch
  • Training and Validation
  • Early stopping
Module 4
  • Multiple Linear Regression Prediction
  • Multiple Linear Regression Training
  • Linear regression multiple outputs
  • Multiple Output Linear Regression Training
Module 5
  • Final project

Instructors

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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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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