The Best PyTorch courses for beginners to learn PyTorch in 2021.

PyTorch is a deep learning library developed by Facebook to develop machine learning models for NLP, Computer Vision and AI, to name a few. It was developed by Facebook's Artificial Intelligence Research Group and is used to run deep learning frameworks.

PyTorch is an excellent framework for entering the actual machine learning and neural network building process. It is ideal for complex neural networks such as RNNNs, CNNs, LSTMs and neural networks that you want to design for a specific purpose.

PyTorch is a very different kind of deep learning library (dynamic vs. static) that was adopted by many researchers if not most, and it's flexible approach and easy-to-understand style have won over newcomers and industry veterans alike.

In the light of learning this important skill, we at Coursesity, have curated some of the Best Online PyTorch Courses with certificates that will help you in improving your understanding of this Deep Learning Framework and excel in the field of AI Research.

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Top PyTorch Courses List

  1. PyTorch: Deep Learning and Artificial Intelligence

  2. Deep Neural Networks with PyTorch

  3. PyTorch for Deep Learning with Python Bootcamp

  4. PyTorch Essential Training: Deep Learning Online Class

  5. Modern Reinforcement Learning: Deep Q Learning in PyTorch

  6. Transfer Learning for Images Using PyTorch: Essential Training Online Class

  7. Foundations of PyTorch

  8. PyTorch Basics for Machine Learning

  9. Building Your First PyTorch Solution

  10. Deep Learning with Python and PyTorch

  11. Intro to Deep Learning with PyTorch

1. PyTorch: Deep Learning and Artificial Intelligence

Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, and Reinforcement learning.

Course rating: 4.7 out of 5.0 ( 696 Ratings total)

Duration: 23 h 5 m

Certificate: Certificate on completion

The course includes:

  • Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
  • Predict Stock Returns
  • Time Series Forecasting
  • Computer Vision
  • Building a Deep Reinforcement Learning Stock Trading Bot
  • GANs (Generative Adversarial Networks)
  • Recommender Systems
  • Image Recognition
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Natural Language Processing (NLP) with Deep Learning
  • Demonstrate Moore's Law using Code
  • Transfer Learning to create state-of-the-art image classifiers

Initially, you will learn some very basic machine learning models and advance towards state-of-the-art concepts.

Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).

Finally, you will learn about how to convert your previous code so that it uses PyTorch, and also perform projects such as time series forecasting and how to do stock predictions.

This course focuses more on the PyTorch library, rather than deriving any mathematical equations.

You can take PyTorch: Deep Learning and Artificial Intelligence certification course on Udemy.

2. Deep Neural Networks with PyTorch

Learn Deep Neural Networks with PyTorch from IBM. The course will teach you how to develop deep learning models using Pytorch.

Course rating: 4.4 out of 5.0 ( 902 Ratings total)

Duration: 31 h

Certificate: Certificate on purchase

In this course, you will learn how to:

  • develop deep learning models using PyTorch.
  • start with PyTorch's tensors and Automatic differentiation package.
  • use PyTorch for Deep Learning application.
  • build Deep Neural Networks using PyTorch.

The course includes:

  • Tensor and Datasets
  • Linear Regression Pytorch Way
  • Multiple Input Output Linear Regression
  • Logistic Regression for Classification
  • Shallow Neural and Deep Networks
  • Convolutional Neural Network

The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and Automatic differentiation package.

Initially, you will learn about Linear Regression and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization, and dropout layers.

Finally, Convolutional Neural Networks and Transfer learning will also be covered in this PyTorch course.

You can take Deep Neural Networks with PyTorch certification course on Coursera.

3. PyTorch for Deep Learning with Python Bootcamp

Learn how to create state-of-the-art neural networks for deep learning with Facebook's PyTorch Deep Learning library!

Course rating: 4.6 out of 5.0 ( 2,378 Ratings total)

Duration: 17 h

Certificate: No certificate

In this course, you will learn how to:

  • use NumPy to format data into arrays.
  • use pandas for data manipulation and cleaning.
  • understand classic machine learning theory principles.
  • use PyTorch Deep Learning Library for image classification.
  • use PyTorch with Recurrent Neural Networks for Sequence Time Series Data.
  • create state-of-the-art Deep Learning models to work with tabular data.

The course includes:

  • NumPy
  • Pandas
  • Machine Learning Theory
  • Test/Train/Validation Data Splits
  • Model Evaluation - Regression and Classification Tasks
  • Unsupervised Learning Tasks
  • Tensors with PyTorch
  • Neural Network Theory
  • Artificial Neural Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks

You can take PyTorch for Deep Learning with Python Bootcamp certification course on Udemy.

4. PyTorch Essential Training: Deep Learning

Explore the basics of deep learning using PyTorch. Learn about the components of an image recognition model using the Fashion MNIST dataset.

Course rating : 19,060 total enrollments

Duration: 1 h 33 m

Certificate: Certificate on completion

The course includes:

  • Fashion MNIST and Neural Networks
  • Working with Classes and Tensors
  • Working with Loss, Autograd, and Optimizers
  • Troubleshooting and CPU/GPU Usage

Initially, the course dives into the basics of deep learning using PyTorch. Starting with a working image recognition model, he shows how the different components fit and work in tandem—from tensors, loss functions, and autograd to troubleshooting a PyTorch network.

You can take PyTorch Essential Training: Deep Learning certification course on LinkedIn Learning.

5. Modern Reinforcement Learning: Deep Q Learning in PyTorch

Turn Deep Reinforcement Learning Research papers into agents that beat classic Atari games.

Course rating: 4.7 out of 5.0 ( 556 Ratings total)

Duration: 5 h 5 m

Certificate: Certificate on completion

In this course, you will learn how to:

  • read and implement deep reinforcement learning papers.
  • code Deep Q learning agents.
  • code Double Deep Q Learning Agents.
  • code Dueling Deep Q and Dueling Double Deep Q Learning Agents.
  • write modular and extensible deep reinforcement learning software.
  • automate hyperparameter tuning with command line arguments.
  • repeat actions to reduce computational overhead
  • rescale the Atari screen images to increase efficiency
  • stack frames to give the Deep Q agent a sense of motion
  • evaluate the Deep Q agent's performance with random no-ops to deal with the model overtraining

In this PyTorch course, you will learn a repeatable framework for reading and implementing deep reinforcement learning research papers. You will read the original papers that introduced Deep Q learning, Double Deep Q learning, and Dueling Deep Q learning algorithms.

Plus, you will learn how to implement these in pythonic and concise PyTorch code, which can be extended to include any future deep Q learning algorithms. These algorithms will be used to solve a variety of environments from the Open AI gym's Atari library, including Pong, Breakout, and Bank heist.

Finally, you will learn the key to making these Deep Q Learning algorithms work, which is how to modify the Open AI Gym's Atari library to meet the specifications of the original Deep Q Learning papers.

You can take the Modern Reinforcement Learning: Deep Q Learning in PyTorch certification course on Udemy.

6. Transfer Learning for Images Using PyTorch: Essential Training

Discover how to implement transfer learning using PyTorch, the popular machine learning framework.

Course rating: 3,292 total enrollments

Duration: 1 h 37 m

Certificate: Certificate on completion

The course includes:

  • What Is Transfer Learning?
  • Transfer Learning: Fixed Feature Extractor
  • Fine-Tuning the ConvNet
  • Further Techniques

This course shows you how to leverage PyTorch for a similarly buzzworthy technique i.e. transfer learning. Learn how to implement transfer learning for images using PyTorch, including how to create a fixed feature extractor and freeze neural network layers. Plus, find out about using learning rates and differential learning rates.

You can take Transfer Learning for Images Using PyTorch: Essential Training certification course on LinkedIn Learning.

7. Foundations of PyTorch

This course covers many aspects of building deep learning models in PyTorch, including neurons and neural networks, and how PyTorch uses differential calculus to train such models and create dynamic computation graphs in deep learning.

Course rating: 4.5 out of 5.0 ( 46 Ratings total)

Duration: 2 h 51 m

Certificate: Certificate on completion

The course includes:

  • Getting Started with PyTorch for Machine Learning
  • Working with Tensors in PyTorch
  • Working with Gradients Using the Autograd Library
  • Building Dynamic Computation Graphs

In this course, Foundations of PyTorch, you will gain the ability to leverage PyTorch support for dynamic computation graphs and contrast that with other popular frameworks such as TensorFlow.

First, you will learn the internals of neurons and neural networks, and see how activation functions, affine transformations, and layers come together inside a deep learning model.

Next, you will discover how such a model is trained, that is, how the best values of model parameters are estimated. You will then see how gradient descent optimization is smartly implemented to optimize this process.

plus, you will understand the different types of differentiation that could be used in this process, and how PyTorch uses Autograd to implement reverse-mode auto-differentiation. You will also work with different PyTorch constructs such as Tensors, Variables, and Gradients.

Finally, you will explore how to build dynamic computation graphs in PyTorch. You will round out the course by contrasting this with the approaches used in TensorFlow, another leading deep learning framework that previously offered only static computation graphs, but has recently added support for dynamic computation graphs.

You can take the Foundations of PyTorch certification course on Pluralsight.

8. PyTorch Basics for Machine Learning

This course 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.

Course rating: 6,011 total enrollments

Duration: 840 h

Certificate: Certificate on completion

In this course, you will learn how to:

  • build a Machine learning pipeline in PyTorch.
  • train Models in PyTorch.
  • load large datasets.
  • train machine learning applications with PyTorch.
  • incorporate and Python libraries such as Numpy and Pandas with PyTorch.

Here, you will start with PyTorch's tensors in one dimension and two dimensions, you will learn the tensor types and operations, PyTorchs Automatic Differentiation package, and integration with Pandas and Numpy. This is followed by an in-depth overview of the dataset object and transformations.

Next, you will learn how to train a linear regression model. You will learn the fundamentals of PyTorch including how to make a prediction using PyTorch's linear class and custom modules. Then determine loss and cost with PyTorch.

After that, you will train a linear regression model via PyTorch's build-in functionality, developing an understanding of the key components of PyTorch. Including how to effectively train PyTorch's custom modules using the optimizer object, allowing you an effective way to train any model.

Finally, you will learn how to extend your model to multiple input and output dimensions in applications such as multiple linear regression and multiple-output linear regression. You will learn the fundamentals of the linear object, including how it interacts with data with different dimensions and the number of samples.

You can take PyTorch Basics for Machine Learning certification course on Edx.

9. Building Your First PyTorch Solution

This course covers the important practical aspects of installing PyTorch from scratch on a variety of different platforms and getting going with classification and regression models.

Course rating: 4.5 out of 5.0 ( 23 Ratings total)

Duration: 2 h 21 m

Certificate: Certificate on completion

The course includes:

  • Installing PyTorch on a Local Machine
  • Understanding Linear Regression with a Single Neuron
  • Building a Regression Model Using PyTorch
  • Building a Classification Model Using PyTorch

In this course, Building Your First PyTorch Solution, you will learn how to install PyTorch using pip and conda, and see how to leverage GPU support. Next, you will discover how to hand-craft a linear regression model using a single neuron, by defining the loss function yourself.

Moreover, you will then see how PyTorch optimizers can be used to make this process a lot more seamless. You will understand how different activation functions and dropouts can be added to PyTorch neural networks. Finally, you will explore how to build classification models in PyTorch.

You can take Building Your First PyTorch Solution certification course on Pluralsight.

10. Deep Learning with Python and PyTorch

Learn how to use Python and its popular libraries such as NumPy and Pandas, as well as the PyTorch Deep Learning library.

Course rating : 40,172 total enrollments

Duration: 18 h

Certificate: Certificate on purchase

In this course, you will learn how to:

  • apply knowledge of Deep Neural Networks and related machine learning methods.
  • build and train Deep Neural Networks using PyTorch.
  • build Deep learning pipelines.

Initially, you will learn the basics of PyTorch and how to build deep neural networks in it. Next, you will learn how to train these models using state-of-the-art methods.

Plus, you will first review multiclass classification, learning how to build and train a multiclass linear classifier in PyTorch. This will be followed by an in-depth introduction on how to construct Feed-forward neural networks in PyTorch, learning how to train these models, how to adjust hyperparameters such as activation functions and the number of neurons.

You will then learn how to build and train deep neural networks—learning how to apply methods such as dropout, initialization, different types of optimizers, and batch normalization. Finally, you will learn about dimensionality reduction and autoencoders including principal component analysis, data whitening, shallow autoencoders, deep autoencoders, transfer learning with autoencoders, and autoencoder applications.

You can take Deep Learning with Python and PyTorch certification course on Edx.

11. Intro to Deep Learning with PyTorch

Learn the basics of deep learning and implement your deep neural networks with this Free PyTorch Course.

Duration: 32 h

Certificate: No certificate

In this course, you will learn how to:

  • understand the basic concepts of deep learning such as neural networks and gradient descent.
  • implement a neural network in NumPy and train it using gradient descent with in-class programming exercises.
  • build a neural network to predict student admissions.

The course includes:

  • Introduction to Deep Learning
  • Introduction to PyTorch
  • Deep Learning with PyTorch
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Style Transfer
  • Natural Language Classification
  • Deploying with PyTorch

In this course, you’ll learn the basics of deep learning, and build your deep neural networks using PyTorch. You’ll get practical experience with PyTorch through coding exercises and projects implementing state-of-the-art AI applications such as style transfer and text generation.

You can take Intro to Deep Learning with PyTorch certification course on Udacity.


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