PyTorch Basics for Machine Learning

This course will introduce you to the basics of PyTorch. This course will teach you how to use classic machine learning algorithms, with a focus on how PyTorch creates and optimizes models. You will quickly iterate through various aspects of PyTorch, providing you with solid foundations and all the prerequisites you need before building deep learning models. You will begin with PyTorch's tensors in one and two dimensions, learning tensor types and operations, as well as PyTorch's Automatic Differentiation package and integration with Pandas and Numpy. This is followed by a detailed explanation of the dataset object and transformations.

Following that, you will discover how to train a linear regression model. You will learn the basics of PyTorch, such as how to make a prediction using PyTorch's linear class and custom modules. Then, using PyTorch, calculate the loss and cost. Following that, you will train a linear regression model using PyTorch's built-in functionality, gaining an understanding of PyTorch's key components. Including how to effectively train PyTorch's custom modules using the optimizer object, allowing you to train any model in an efficient manner.

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

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.

You can take PyTorch Basics for Machine Learning certification course on Edx.
Course rating: N/a
Duration: 840 h
Certificate: Certificate on completion
Enroll here: edx.org/course/pytorch-basics-for-machine-learning