Best Pytorch Courses Online & Certification (June 2024)

  • Post last modified:11 September 2023
  • Reading time:37 mins read
  • Post category:Best Online Course
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Whether you’re a Python developer, machine learning engineer, or any professional, it is never too late to learn another skill. Python’s most powerful library – Pytorch, is not a trend, but a valuable asset. The three most popular industries that use Pytorch are AI, Machine Learning, and Deep Learning. So, if you want to enter AI, Pytorch is a must.  

In this article, we will share the Best Pytorch Courses, which are reviewed by experts and most recommended online. Before you dive in, you must understand why Pytorch can be a beneficial skill to acquire.

Why is learning Pytorch important?

Facebook developed and made PyTorch publicly available in 2016. One of the main advantages of Pytorch is that it offers developers working on machine learning projects flexibility and speed. Machine Learning and AI is the hottest field to pursue in 2024.


Over the past four years, employment in AI and machine learning has increased by approximately 75%, and this growth is expected to continue. A high-paying career that will be in demand for decades is machine learning. Pytorch developers earn between $81K and $115K, based on their experience according to Glassdoor.com. 

If you are pursuing a career in deep learning, you can’t miss the opportunity to gain Pytorch experience. Scroll down to find the 6 Best  Pytorch courses for you and start learning!

Our product recommendations are unbiased and based on an independent review process. We may receive a commission for links to recommended partners. See our advertiser disclosure for more information.


Best Pytorch Courses, Certification, Tutorials, Training, Classes Online

Deep Neural Networks with Pytorch [Coursera]

The Pytorch Online Course is the most effective way to understand how to build deep learning models using Pytorch. You will start from scratch and learn about Pytorch’s tensors and Automatic differentiation package. In this Pytorch library course, the instructor will discuss different models with fundamentals, such as Linear Regression, and logistic regression. Moreover, the role of different activation functions, normalization, and dropout layers will be explained.

Course Instructor

The Deep Neural Networks with Pytorch Course was designed by Joseph Santarcangelo, who works as a data scientist at IBM. With this course, Joseph can help deep learning aspirants enhance their knowledge. IBM has offered this Coursera Pytorch Course to guide AI beginners.

What You’ll Learn

This Coursera Pytorch Online Course is part of the IBM AI Engineering Professional Certificate. It will cover all topics in 6 weeks of modules. 

  1. Tensors and Datasets: This module will give an overview of Tensors including two-dimensional tensors. You will learn to differentiate in Pytorch and use simple datasets. 

  2. Linear Regression: In this module, you will learn how to predict and develop linear regression models using Pytorch. Also, it helps you discover how to train, validate, and test split datasets using Pytorch.

  3. Multiple Linear Regression and Logistic Regression: First, you will study and work with multiple linear regression to predict and train models. Secondly, the next module in the third week will teach you logistic regression for classification.

  4. Shallow Neural Networks and Softmax Regression: In the fourth week, you will cover two topics: Softmax and Neural Networks with multiple dimensional input.

  5. Using deep learning networks: Understand the basics of deep learning networks and how to work with Gradient Descent in the fifth week’s module.

  6. Convolutional Neural Networks: This week’s lesson will help you explore convolutional neural networks and build torch vision models.

Pros & Cons

Pros

  • Excellent theoretical and practical examples
  • Hands-on projects
  • Get graded quizzes and assignments

Cons

  • It can be challenging

Key Highlights & Learning Objectives

  • Demonstrate an understanding of Deep Neural Networks and related machine learning methods. 

  • Learn how to use Python libraries such as PyTorch to build Deep Learning applications

  • Create Deep Neural Networks using the PyTorch library and implement linear and logistic regression.

  • Access to unlimited course materials, assignments, 30 videos lectures, quizzes, certificate of completion

Who is it for?

The Online Pytorch Course is the most appropriate option for intermediate learners with machine learning and deep learning. Once you’ve completed this course, you will gain confidence to build deep neural networks using the Pytorch libraries.

Rating: 4.4/5
Students Enrolled: 68,763
Duration: 30 hours

Coursera 7-Day Trail offer

Machine Learning with PyTorch [Udacity]

This Pytorch for Machine Learning Course will build a strong foundation for machine learning. You will learn how to use algorithms from data cleaning to supervised models. This Udacity Pytorch Course will also teach you deep learning and unsupervised learning.

Course Instructor

Take part in this Udacity Pytorch Nanodegree Program with the best instructors in the world. Mat Leonard, Luis Serrano, Dan Romuald, Sean Carrell, Jennifer Stab, and other resources will assist you in the classroom. They will also help you stay on track to complete the topic on time.

What You’ll Learn

This Udacity Pytorch Nanodegree Program will cover Pytorch key concepts in three chapters, including: 

  1. Supervised Learning: In this lesson, you will learn about supervised learning, a common approach to model construction.

  2. Introduction to Neural Networks with PyTorch: This lesson will provide you with a basic understanding of the foundations of neural network design and training in PyTorch.

  3. Unsupervised Learning: This tutorial teaches you how to use unsupervised learning methods to solve a variety of problems.

Pros & Cons

Pros

  • Simple and well-designed
  • Get real-time support
  • Keep up with your project’s progress and give feedback.

Cons

  • Requires strong knowledge of probability, statistics, and Python programming

Key Highlights & Learning Objectives

  • Become familiar with supervised learning

  • Discover how PyTorch can be used to design and train neural networks.

  • Develop an understanding of the different problem domains affected by unsupervised learning methods.

  • Immerse yourself in real-world projects and interactive content to master the teaching skills companies want. 

  • Access to GitHub portfolio review and LinkedIn profile optimization service.

Who is it for?

This Udacity Machine Learning with Pytorch Course is intended for Python programmers. If you are just a beginner in machine learning with no experience, it can be an excellent option to start with. Upon completion, you will have enough practical experience to apply machine-learning techniques using Pytorch.

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Rating: 4.7/5
Duration: 3 months at 10 hrs/week

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Deep Learning with PyTorch Bootcamp [Udemy]

Start with Pytorch for Deep Learning to create neural networks. This Python library, Pytorch provides support to write neural networks. You will focus on theoretical concepts with practical hands-on exercises and projects that allow you to apply skills to your datasets. It will give you access to Jupyter Notebooks and codes to create visualizations from datasets.

Course Instructor

Jose Marcial Portilla is the instructor of this Udemy Deep Learning with PyTorch Bootcamp. He is one of the top-rated Udemy instructors with profound experience in machine learning and Python programming.

Pros & Cons

Pros

  • Professionally and carefully curated
  • Clear and concise code explanations
  • Full access to codes and practice exercises

Cons

  • It may fast-paced
  • Not ideal for beginners

Key Highlights & Learning Objectives

  • The Udemy Deep Learning with Python Bootcamp is divided into 12 sections and 97 video lectures, which cover all the concepts to build your first neural network. 

  • Learn how to format data into arrays using NumPy’s array format

  • Discover Python’s powerful library – Pandas – and how to use it to manipulate and clean data in a variety of ways.

  • Discover the principles of classic machine learning theory and how to use the PyTorch Deep Learning Library for image classification

  • Understand the application of PyTorch to recurrent neural networks and how to analyze sequence time series data.

  • Provide lifetime access to 2 articles, 2 downloadable resources, and a certificate of completion to add to your resume.

Who is it for?

The Online Pytorch for Deep Learning Course on Udemy is designed for intermediate to advanced Python developers interested in deep learning with PyTorch. After completing the tutorial, you will be able to develop a wide range of deep learning models to address your challenges. This is done using your own data sets.

Rating: 4.7/5
Students Enrolled: 46,842
Duration: 17 hours

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Valid till: 1st Feb

Deep Learning with Python and PyTorch [edX]

The edX Pytorch Online Course is the most effective resource for learning how to use Pytorch to create deep learning models. You will learn from scratch about Pytorch’s tensors and the Automatic Differentiation package. The lecturer will outline many deep learning techniques using the Pytorch libraries, including logistic and linear regression.

Course Instructor

Joseph Santarcangelo, an IBM data scientist, created the Deep Neural Networks with Pytorch Course. Joseph can help deep learning seekers improve their knowledge by offering this training.

Pros & Cons

Pros

  • Excellent theoretical and practical examples
  • Hands-on projects
  • Get graded quizzes and assignments

Cons

  • Challenging 
  • Need basic machine learning knowledge

Key Highlights & Learning Objectives

  • The edX Deep Learning with PyTorch Online Course is divided into 7 modules.

  • Understand Deep Neural Networks and related machine learning techniques. 

  • Develop deep learning applications using Python libraries such as PyTorch

  • Apply linear and logistic regression to Deep Neural Networks using the PyTorch library.

  • Learn how to build and train deep neural networks using Pytorch.

  • Able to use convolutional neural networks and create your first torch vision model.

  • Get a digital certificate once you complete all modules and one project.

Who is it for?

Those with experience in machine learning and deep learning will find that the PyTorch Course Online is the most suitable option. As a result of taking this course, you can build deep neural networks using the Pytorch libraries with confidence.

Rating: 4.5/5
Students Enrolled: 66,310
Duration: 6 weeks 2–4 hours per week

Learn Pytorch for Deep Learning [Datacamp]

Start your Pytorch journey with this Datacamp Pytorch Course Online. You will explore the fundamentals of the PyTorch library and its applications to neural networks and deep learning. Moreover, the Online Pytorch Course will cover the basics of developing artificial neural networks and how to train them using real data.

Course Instructor

The Datacamp Pytorch Course will be taught by Ismail Elezi, a third-year Ph.D. student at Ca’ Foscari, University of Venice. During his Ph.D., he did an internship at ZHAW Datalab (Switzerland) and worked under Professor Thilo Stadelmann. 

Pros & Cons

Pros

  • Start with free
  • Bite-sized chapters
  • Practical examples and codes

Cons

  • Understand Python programming
  • Must know supervised learning with Scikit-Learn

Key Highlights & Learning Objectives

  • The Datacamp Pytorch Full Course covers all fundamentals in 4 chapters to develop deep knowledge of neural networks with the Pytorch library. 
    • Introduction to Pytorch
    • Artificial neural networks
    • Convolutional neural networks (CNN).
    • CNN – Part 2

  • Learn the basics of neural networks and deep learning using PyTorch.

  • Discover how to train Artificial Neural Networks on real datasets.

  • Learn how to build convolutional neural networks, including how to train them, use them in predictive analysis, and evaluate them.

  • Explore regularization, batch-normalization, and transfer learning and how to use these concepts effectively to make neural networks function in the real world.

Who is it for?

The Pytorch for Deep Learning course from Datacamp is for Python developers and programmers with basic supervised learning knowledge. Once you’ve completed the course, you’ll be confident enough to explore neural networks further and gain more expertise.

Rating: 4.7/5
Students Enrolled: 36,264
Duration: 4 hours

50% OFF Datacamp
Offer till 1st Feb

Intro to Deep Learning Pytorch [Udacity]

In the Udacity Pytorch Course, you will build a foundational knowledge of deep learning and understand how to use Pytorch for deep learning. You will be able to create your powerful neural networks using PyTorch. To get a better understanding of how to work with Pytorch, you should take this Pytorch Deep Learning Course.

Course Instructor

The Best Instructors from Udacity have created this Pytorch Certification Course to share their knowledge and experience with others. Luis Serrano, Alexis Cook, Soumith, Mat Leonard, and Cezzane Camacho are among the top-rated instructors with experience in data science and machine learning.

Pros & Cons

Pros

  • A Complete free course
  • Highly-curated content
  • Taught and well-designed by industry experts

Cons

  • Basics

Key Highlights & Learning Objectives

  • The Udacity Intro to Deep Learning with Pytorch Course includes 9 sections teaching everything you need to know to build neural networks using the Pytorch library.

  • Learn about deep learning concepts like neural networks and gradient descent. Implement a neural network in NumPy and train it with gradient descent.

  • Build your first neural network with PyTorch to classify clothing images and work through a set of Jupyter Notebooks to learn the major components of PyTorch.

  • Learn to use PyTorch to implement a recurrent neural network that classifies text and predicts movie reviews

Who is it for?

The Deep Learning Pytorch Course is for intermediate learners who are comfortable with Python and data processing libraries such as NumPy and Matplotlib. It is recommended to understand linear algebra and calculus. By the end of the Pytorch Course Free, you will gain practical experience building and training deep neural networks with Pytorch.

Rating: 4.5/5
Duration: 2 Months

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FAQ

What is Pytorch used for?

PyTorch is a powerful framework for building deep learning models. The technique is typically used in applications such as image recognition and language processing. Because it’s written in Python, machine learning developers can easily learn and use it.

How long will it take to learn Pytorch?

It takes one to three months to learn and master PyTorch. However, it depends on your Python proficiency and machine learning knowledge.

What is the best way to learn Pytorch?

The best way to learn Pytorch is through online courses or training certifications from top learning platforms including Coursera, Udemy, Datacamp, and more. In the above list, all courses are top-rated and recommended by previous learners. You can review each course and choose one that fits your goals and preferences.

Do you need math for Pytorch?

Deep learning is based on tensors, which are data structures. Advanced mathematical skills are required to build a deep neural network.

Is Pytorch better than Tensorflow?

There are pros and cons to both open-source Python libraries. The main benefit of Tensorflow is that it offers a better interface to create better visualization from datasets. On the other hand, Pytorch is easier to use than TensorFlow because it works as a standard Python debugger. So, you don’t need another debugger for immediate execution.

Last Words

Whatever your reason and goal are to take a Pytorch course, you will never regret learning this easy-to-understand programming for data science and machine learning. Each course was designed with effort and experience by industry experts. As a machine learning engineer, it is critical to timely upgrade your knowledge and follow trends that can help you in your career. You may require Python programming skills

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