Top 3 PyTorch Certification Programs
If you work with Python and machine learning, chances are you’ve heard about PyTorch. We’re guessing that’s because it has quickly become one of the most popular libraries for using machine learning in Python.
In this article, we cover everything you need to know to get PyTorch certification. These will help you get off the ground and take your PyTorch skills to the next level for a career in machine learning.
Our Top 3 Picks
1. Introduction to PyTorch Nanodegree (Udacity)
Get practical training with machine learning by taking the 3-month Udacity Introduction to PyTorch Nanodegree Program in collaboration with Kaggle and AWS. This program provides you with a solid foundation in data manipulation, unsupervised, and supervised learning.
You will get hands-on experience with the PyTorch machine learning techniques and learn to solve problems in data science. After completing this program, you will be more proficient in data cleaning, supervised model construction, and deep learning abilities.
- Supervised Learning – Get started with supervised learning and begin construction on your next model. In this course, you will learn about supervised learning models and labeled training data. Finally, you will apply your knowledge with a hands-on project where you have to optimize various supervised learning models for a fictitious charity organization.
- Deep Learning – In this course, you will understand how to build neural networks and train them using PyTorch. Next, you will optimize the training process for object recognition in an Image Classifier project. This is an essential course for anyone who wants to build intelligent applications in PyTorch.
- Unsupervised Learning – In this course, you will learn how to use unsupervised learning methods to solve different types of problems. This course will challenge you with an unsupervised learning project, where you will gain better insights into customer behavior and preferences. With this powerful tool, you will be able to segment your target customers into different categories for further marketing and investigation.
For more information, read our review of the Udacity Introduction to PyTorch Nanodegree.
2. Introduction to PyTorch Program (DataCamp)
Learn PyTorch in a fun and engaging way. The Introduction to PyTorch program from DataCamp will introduce you to the PyTorch deep learning library with a hands-on approach.
This 4-course series covers all the essentials you need to get started with PyTorch. First, it introduces you to how to train Artificial Neural Networks. Finally, you will learn Convolutional Neural Networks to make predictions.
- Introduction to PyTorch – Get up to speed with the basics of neural networks and deep learning. This course will teach you how to use the forward and back propagation using the PyTorch library.
- Artificial Neural Networks – This course explores how to use Artificial Neural Networks including ReLU, loss functions, and more.
- Convolutional Neural Networks (CNNs) – Get up to speed with the basics of Convolutional Neural Networks and use them to make predictions.
- Using Convolutional Neural Networks (CNNs) – In this last course, it explores how to use CNNs in practice. This includes regularization, normalization, and transfer learning.
3. PyTorch Basics and Deep Learning (IBM)
IBM gives you a unique opportunity to learn from their team of experts in two separate courses that focus on PyTorch.
First, PyTorch Basics for Machine Learning will give you a strong foundation to create and optimize models in PyTorch.
Secondly, the Deep Learning with Python and PyTorch course will help you train deep neural networks and deep learning pipelines in PyTorch.
- PyTorch Basics for Machine Learning – This course provides the foundations you need to build powerful machine learning models. You will learn how to implement classic machine learning algorithms to train and load large datasets in PyTorch. You will also learn how to optimize models to improve their performance.
- Deep Learning with Python and PyTorch – Learn how to create deep learning models with the PyTorch library. This course covers topics like dimensionality reduction, computer vision, classification, and regression.