Udemy is one of the learning platforms that offers multiple PyTorch courses. Udemy’s PyTorch courses cover topics such as Tensors and Operations, Loss Functions and Optimization, Data Loading and Preprocessing, etc.
So, regardless of their experience level, learners can enroll on Udemy. Moreover, the courses are affordable, have lifetime access to course materials, and have the convenience of self-paced learning.
| Best PySpark Courses on Udemy | Best AI Courses on Udemy |
1. PyTorch: Deep Learning and Artificial Intelligence
This is one of the highest-rated PyTorch courses on Udemy. The course will teach how to build a Deep Reinforcement Learning Stock Trading Bot, Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs). Besides these, the course will also demonstrate Moore’s law using code, and other concepts related to Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, and Reinforcement Learning.
- Course Rating: 4.8/5
- Duration: 24.5 hours
- Benefits: 150 video lectures, full lifetime access on mobile and TV, certificate of completion from Udemy
Learning Outcomes
| Learn how to use a stock trading bot | Natural language processing |
| Transfer learning for computer vision | Produce cutting edge image classifiers using transfer learning |
2. Deep Learning with PyTorch
“Deep Learning with PyTorch” teaches deep learning and real-life uses of deep learning with PyTorch. The course also goes into the details of neural networks and the different aspects of it.
- Course Rating: 4.8/5
- Duration: 3.5 hours
- Benefits: 1 downloadable resource, full lifetime access on mobile and TV, certificate of completion from Udemy
Learning Outcomes
| Discover how to implement adjustments and exploratory analysis to enhance the neural network model | Learn how to evaluate a neural network’s training and track its performance |
| Learn how to create a Deep Learning project using Jupyter Notebook | Discover measures to analyse general performance and how to evaluate a neural network model |
| Study the underlying mathematics of neural networks | – |
3. PyTorch for Deep Learning with Python Bootcamp
“PyTorch for Deep Learning with Python Bootcamp” course on Udemy has over 25,000 student enrollments. The course teaches the use of NumPys, and Panda for data manipulation. It also focuses on the use of PyTorch along with Recurrent Neural Networks for Sequence Time Series Data and other related concepts.
- Course Rating: 4.6/5
- Duration: 17 hours
- Benefits: 17 hours of on-demand video, 2 articles, 2 downloadable resources, full lifetime access on mobile and TV, certificate of completion from Udemy
Learning Outcomes
| Recurrent neural networks for sequence time series data in PyTorch | Learn how to format data into arrays using NumPy |
| Make cutting-edge Deep Learning models that can be used with tabular data | Utilise Pandas to clean and manipulate data. |
4. Hands-on Computer Vision with PyTorch 1x
This course is for PyTorch beginners who want to learn computer vision. The course gives practical insights into the use of computer vision and teaches various aspects of PyTorch, and other features related to computer vision.
- Course Rating: 4.6/5
- Duration: 3 hours
- Benefits: 3 hours of on-demand video, full lifetime access on mobile and TV, certificate of completion from Udemy
Learning Outcomes
| Learn about ResNets, Neural Style Transfer, data augmentation, and other computer-vision-related subtopics | Become an expert practitioner with practical knowledge of computer vision after starting as a beginner. |
| Utilise PyTorch’s features, including tensors, dynamic graphs, auto-differentiation, and others. | Splitting, enhancing, and drawing conclusions from datasets with ease. |
| Easily extract data from ground-breaking research publications | – |
5. PyTorch for Deep Learning in 2024: Zero to Mastery
“PyTorch for Deep Learning in 2024: Zero to Mastery” has over 10,000 students enrollment and is popular among PyTorch learners on Udemy. The course teaches PyTorch fundamentals from scratch and also provides you with the skills needed to be a deep-learning engineer.
- Course Rating: 4.6/5
- Duration: 52 hours
- Benefits: 52 hours of on-demand video, 7 articles, full lifetime access on mobile and TV, certificate of completion from Udemy
Learning Outcomes
| How to design a machine learning (ML) algorithm to detect patterns in data, then utilize that algorithm as artificial intelligence (AI) to improve your apps. | Construct machine learning algorithms and use them in the same way that you would construct a Python program. |
| Master deep learning to stand out to employers looking for Deep Learning Engineers. | – |
6. PyTorch Tutorial – Neural Networks & Deep Learning in Python
“PyTorch Tutorial – Neural Networks & Deep Learning in Python” is an introductory course to neural networks and deep learning in Python. The course teaches the concepts of AI in deep learning and neural networks, and the application of Python in neural networks and deep learning.
- Course Rating: 4.6/5
- Duration: 6.5 hours
- Benefits: 6.5 hours of on-demand video, full lifetime access on mobile and TV, certificate of completion from Udemy
Learning Outcomes
| Python applications for neural networks – setting up the Anaconda environment to launch PyTorch | Overview of Deep Learning and Neural Networks: Theoretical foundations for key ideas (like deep learning) without the technical lingo |
| Implement deep learning neural networks using PyTorch to analyze visual data. | Theoretical foundations of key ideas (like deep learning) without the language |
7. PyTorch for Deep Learning and Computer Vision
“PyTorch for Deep Learning and Computer Vision” teaches complicated aspects of PyTorch and Python applications. Also, it deals with various problems that one may face while learning deep learning and computer vision.
- Course Rating: 4.5/5
- Duration: 10.5 hours
- Benefits: 10.5 hours of on-demand video, 3 downloadable resources, full lifetime access on mobile and TV, certificate of completion from Udemy
Learning Outcomes
| Create intricate models by using the advanced imaging and computer vision theme. | Utilise complicated pre-trained models to solve challenging computer vision challenges. |
| Build neural networks from the start | – |
8. PyTorch for Deep Learning Bootcamp: Zero to Mastery
“PyTorch for Deep Learning Bootcamp: Zero to Mastery” teaches the application of PyTorch in deep learning from beginner to advanced level. Most importantly, it focuses on the use of Deep Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) for image classification in PyTorch and time series prediction in PyTorch.
- Course Rating: 4.5/5
- Duration: 9 hours
- Benefits: 9 hours of on-demand video, 3 downloadable resources, 3 articles, full lifetime access on mobile and TV, certificate of completion from Udemy
Learning Outcomes
| Recurrent neural network (RNN) time series prediction using PyTorch | Natural Language Processing (NLP) through PyTorch |
| Utilise PyTorch for Multilayer Perceptron (MLP) Linear Regression | Recognise the fundamental ideas behind neural networks and how they operate. |
9. PyTorch Ultimate 2024: From Basics to Cutting-Edge
This course teaches the advanced application of PyTorch including Natural Language Processing (NLP), CNNs (Image, Audio-Classification and Object Detection), RNNs, Transformers, Style Transfer, Autoencoders, GAN, image classification, and new models like ChatGPT and OpenAI.
- Course Rating: 4.5/5
- Duration: 17.5 hours
- Benefits: 17.5 hours of on-demand video, 5 articles, full lifetime access on mobile and TV, certificate of completion from Udemy.
Learning Outcomes
| Learn how to create CNN models for object identification, style transfer, picture categorization | Natural Language Processing (NLP), CNNs (Image, Audio-Classification; Object Detection), RNNs, Transformers, Style Transfer, Autoencoders, GANs, and recommenders |
| Learn about fresh models like OpenAI ChatGPT and new frameworks like PyTorch Lightning | Apply cutting-edge techniques like Transformers to unique datasets |
10. Deep Learning with PyTorch for Medical Image Analysis
“Deep Learning with PyTorch for Medical Image Analysis” teaches the application of PyTorch in real-life medical imaging tasks. Also, it teaches the use of NumPy, the basics of medical image analysis, 2D and 3D data handling, etc.
- Course Rating: 4.4/5
- Duration: 12 hours
- Benefits: 12 hours of on-demand video, 5 articles, 7 downloadable resources, 1 practice test, full lifetime access on mobile and TV, certificate of completion from Udemy
Learning Outcomes
| Auto cancer segmentation | Data handling in medical image |
| Data formatting in medical imaging | Use of NumPy |
11. A deep understanding of deep learning (with Python intro)
“A deep understanding of deep learning (with Python intro)” is a comprehensive deep learning. This course covers deep learning concepts, including neural network architectures, mathematical foundations, and practical Python-based implementations using PyTorch. Also, this course offers valuable insights and practical skills for the complex landscape of deep learning.
- Course Rating: 4.6/5
- Duration: 12.5 hours
- Benefits: 6 articles, 46 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| The theory and math underlying deep learning | How to build artificial neural networks |
| Architectures of feedforward and convolutional networks | Building models in PyTorch |
12. Deep Learning for Beginners: Core Concepts and PyTorch
This 9.5-hour course teaches the introduction to Deep Learning. This is ideal for beginners, including those without an engineering or computer science background. The course covers fundamental concepts and mechanics of Deep Learning. So, career transitioners, Python developers, and anyone eager to enhance their grasp of Deep Learning fundamentals can join the course.
- Course Rating: 4.6/5
- Duration: 12.5 hours
- Benefits: 6 articles, 46 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| Develop an intuitive understanding of Deep Learning | Visual and intuitive understanding of core math concepts behind Deep Learning |
| Detailed view of how exactly deep neural networks work beneath the hood | Computational graphs (which libraries like PyTorch and Tensorflow are built on) |
| Build neural networks from scratch using PyTorch and PyTorch Lightening | You’ll be ready to explore the cutting edge of AI and more advanced neural networks like CNNs, RNNs, and Transformers |
13. Introduction to Generative Adversarial Networks with PyTorch
“Introduction to Generative Adversarial Networks with PyTorch” teaches Generative Adversarial Networks (GANs) from basics. Also, the course provides in-depth hands-on experience and insight into the mathematical foundations of modern GAN models. Hence, it is ideal for data scientists seeking to elevate their GAN skills, research, or postgraduate students desiring a comprehensive overview of recent advancements.
- Course Rating: 4.6/5
- Duration: 12.5 hours
- Benefits: 6 articles, 46 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| How Generative Adversarial Networks work internally | How to implement state-of-the-art GANs techniques and methods using PyTorch |
| How to improve the training stability of GANs | – |
14. The Complete Neural Networks Bootcamp: Theory, Applications
“The Complete Neural Networks Bootcamp: Theory, Applications” covers essential topics such as Neural Networks, Backpropagation, Loss Functions, etc. Besides this, the course also focuses on practical applications. The course also explores Transformers and even guides you in building a Chatbot with Transformers.
- Course Rating: 4.6/5
- Duration: 12.5 hours
- Benefits: 6 articles, 46 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| Understand how neural networks work (theory and applications) | Understand how convolutional networks work (theory and applications) |
| Understand how recurrent networks and LSTMs work (theory and applications) | Learn how to use PyTorch in-depth |
15. Modern Computer Vision™ PyTorch, Tensorflow Keras & OpenCV4
“Modern Computer Vision™ PyTorch, Tensorflow Keras & OpenCV4” provides comprehensive knowledge about Computer Vision, AI, and Deep Learning. It covers foundational theories and practical experience. Also, the course includes a wide range of projects and topics. This includes image operations, object detection, facial recognition, video analysis, etc.
- Course Rating: 4.6/5
- Duration: 12.5 hours
- Benefits: 6 articles, 46 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| All major Computer Vision theory and concepts! | Learn to use PyTorch, TensorFlow 2.0, and Keras for Computer Vision Deep Learning tasks |
| OpenCV4 in detail, covering all major concepts with multiple example codes | All Course Code works in accompanying Google Colab Python Notebooks |
16. Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2)
“Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2)” offers a structured framework for understanding and implementing deep reinforcement learning research papers. It covers foundational algorithms like Deep Q, Double Deep Q, and Dueling Deep Q learning. The course also provides essential knowledge for beginners, including the fundamentals of reinforcement learning, Markov decision processes, Q learning, and deep learning using PyTorch.
- Course Rating: 4.6/5
- Duration: 12.5 hours
- Benefits: 6 articles, 46 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| How to read and implement deep reinforcement learning papers | How to code Deep Q learning agents |
| How to Code Double Deep Q Learning Agents | How to Code Dueling Deep Q and Dueling Double Deep Q Learning Agents |
17. Mastering Image Segmentation with PyTorch
“Mastering Image Segmentation with PyTorch” is a comprehensive course designed for individuals at all levels, from beginners to experts, who are interested in delving into the world of image segmentation using PyTorch. The course covers fundamental topics such as tensor handling, autographed for automatic gradient calculations, modeling with linear regression from scratch, data loaders, hyperparameter tuning, CNN theory, and semantic segmentation.
- Course Rating: 4.6/5
- Duration: 12.5 hours
- Benefits: 6 articles, 46 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| Implement semantic segmentation with PyTorch on a real-world dataset | Get familiar with different architectures such as UNet, FPN |
| Understand the theoretical background on topics such as upsampling, loss functions, and evaluation metrics | Perform data preparation to reshape inputs to the appropriate format |
18. The Complete Prompt Engineering Course
The course covers various topics, including Language Models (LLMs), prompt design, machine learning, Python, PyTorch, and Langchain. So, the course is ideal for business professionals, tech enthusiasts, and individuals looking to switch careers.
- Course Rating: 4.6/5
- Duration: 12.5 hours
- Benefits: 6 articles, 46 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| From foundations to experts, learn about every major Prompt Engineering topic. | Master the basics of prompt engineering and the psychology of AI interactions. |
| Learn the art and techniques of crafting effective AI prompts. | Understand and apply best practices in prompt engineering using Python and PyTorch. |
| Develop practical skills through video demos of AI applications and prompt tools. | Explore various AI LLMs, agents, tools, and frameworks such as Open AI, Hugging Face, and Langchain. |
19. Deep Learning for Image Segmentation with Python & Pytorch
“Deep Learning for Image Segmentation with Python & Pytorch” covers the fundamentals of Semantic Segmentation, and then progresses to practicals. Hence, students and professionals from various backgrounds, including Computer Science and Electrical Engineering, can enroll in this course.
- Course Rating: 4.6/5
- Duration: 12.5 hours
- Benefits: 6 articles, 46 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| Learn Image Semantic Segmentation Complete Pipeline and its Real-world Applications with Python & PyTorch using Google Colab | Deep Learning Architectures for Semantic Segmentation (UNet, DeepLabV3, PSPNet, PAN, UNet++, MTCNet, etc.) |
| Datasets and Data Annotations Tool for Semantic Segmentation | Data Augmentation and Data Loaders Implementation in PyTorch |
20. Data Science: Modern Deep Learning in Python
This advanced deep learning course is created based on the “Deep Learning in Python” course. The course emphasizes understanding and implementing machine learning algorithms. Hence, this makes it ideal for learners seeking a deep, hands-on learning experience. So, students, professionals, and data scientists looking can enroll in this course.
- Course Rating: 4.6/5
- Duration: 12.5 hours
- Benefits: 6 articles, 46 downloadable resources, Full lifetime access, Access on mobile and TV, Certificate of completion
Learning Outcomes
| Apply momentum to backpropagation to train neural networks | Apply adaptive learning rate procedures like AdaGrad, RMSprop, and Adam to backpropagation to train neural networks |
| Understand the basic building blocks of TensorFlow | Build a neural network in TensorFlow |
How to build a neural network in TensorFlow?
First, import the TensorFlow library into your Python environment to build a neural network in TensorFlow. Then, define the architecture, compile specifying loss and optimizer, train the model using fit, and evaluate its performance with prediction.
What is the use of Use of NumPy?
NumPy is used for numerical computations and data manipulation in Python. Its main purpose is to handle large arrays and matrices of numeric data, for mathematical operations (such as linear algebra, statistical analysis, and Fourier transforms). It is also used for scientific computing, machine learning, image processing, and other data-intensive tasks.
How can I learn how to create CNN models?
You can learn to create CNN models with introductory resources and courses on deep learning and CNN architecture. Then, practice with frameworks like TensorFlow or PyTorch using image datasets. Also, you should experiment with different techniques like data augmentation and regularization.
Can a beginner construct machine learning algorithms?
A beginner can construct machine learning algorithms. But you will need proper guidance and learning resources. So, you can start with introductory courses or tutorials that cover the basics of machine learning concepts, algorithms, and programming languages (like Python). After that, beginners can gradually build their understanding and skills.
What are the prerequisites to learning a Deep Learning project using Jupyter Notebook?
You should have a basic understanding of Python and be familiar with the concepts of machine learning and neural networks. Also, you should have proficiency in using Jupyter Notebook for coding and experimentation.
Is learning PyTorch still relevant?
Yes, learning PyTorch is relevant as it is used in academia and industry. It is used for its flexibility, dynamic computation graph, and ease. Also, it helps in building neural networks and computer vision research, natural language processing, and reinforcement learning.