Classification with PyTorch

This course is ideal for learners with a basic understanding of Python and PyTorch who want to strengthen their machine learning fundamentals. It is suitable for students, aspiring deep learning engineers, and anyone looking to gain practical skills in classification models with PyTorch.

You will learn how to construct linear classifiers, implement logistic regression, make predictions using probabilistic interpretations, apply Bernoulli distribution maximum likelihood estimation, use cross-entropy for optimization, and implement softmax for multiclass classification. You will gain hands-on experience building, training, and evaluating PyTorch models.

You should have basic Python programming knowledge and familiarity with the PyTorch framework. Prior completion of PyTorch: Tensor, Dataset and Data Augmentation, and Linear Regression with PyTorch courses is recommended, or equivalent knowledge of PyTorch tensors, datasets, and linear regression.

Yes. The course teaches logistic regression implementation, predictions, and parameter estimation using PyTorch, giving you the foundational skills needed for classification tasks.

The course guides you through constructing linear classifiers, understanding the model structure, training parameters, and making predictions, all with practical coding exercises in PyTorch.

Yes. Learners complete hands-on coding exercises for linear models, logistic regression, Bernoulli distribution, cross-entropy, and softmax function to ensure practical understanding of classification models.

The course covers Bernoulli distribution maximum likelihood estimation, helping learners understand probabilistic interpretation and parameter estimation for binary classification tasks in PyTorch.

Cross-entropy for logistic regression is explained and implemented through practical coding exercises. You learn to optimize model parameters for binary and multiclass classification tasks in PyTorch.

Yes. Upon completion, learners receive an IBM Certificate, validating their skills in building, training, and evaluating classification models with PyTorch.

Through a combination of theory, step-by-step examples, and hands-on exercises, you gain practical experience in building, training, and evaluating linear and logistic regression models, optimizing them using cross-entropy, and performing multiclass classification with softmax.