AI roles are forecast to grow more than 5x faster than the overall job market over the next decade (U.S. Bureau of Labor Statistics). Employers need professionals who can build, train, and deploy real-world models.
This IBM specialization helps aspiring AI professionals build the PyTorch, deep learning, GenAI, and NLP skills used by Machine Learning Engineers, NLP Engineers, Deep Learning Engineers, Data Scientists, and AI Research Analysts.
You’ll start with PyTorch tensor fundamentals and build toward trained neural networks, CNNs, and transformer-based language models. You’ll learn to implement gradient descent, backpropagation, dropout, batch normalization, GPU acceleration, attention mechanisms, tokenization, positional encoding, and multi-head attention. Plus, you’ll fine-tune pretrained transformer models, including BERT and DistilBERT, with Hugging Face, and examine GPT-style architectures.
In the capstone, you’ll use GenAI code generation and review support to build a shareable NLP project. You’ll create a text classification pipeline, train an LSTM model, fine-tune a DistilBERT model on the same dataset, and compare their performance with accuracy and F1.
You’ll also gain practical experience with NLP workflows, transformer-based architectures, prompt-assisted coding, code review, and model evaluation– valuable skills for GenAI tools.
Enroll now to develop PyTorch, transformer and NLP modeling skills employers are actively seeking!
Projet d'apprentissage appliqué
Through hands-on labs and projects, you’ll build applied Generative AI and NLP skills with PyTorch, starting with tensor operations and progressing to transformer fine-tuning.
Examples of labs and projects include:
Train regression and classification models with gradient descent
Implement deep neural networks with dropout, batch normalization, and Xavier/He initialization
Build CNNs for image classification using transfer learning with ResNet18
Construct positional encoding, attention mechanisms, and multi-head attention
Pretrain BERT with masked language modeling and apply GPT-style decoder models
Build end-to-end NLP pipelines with tokenization, vocabulary, and data augmentation
Fine-tune BERT and DistilBERT on text classification data
Compare RNN, LSTM, and fine-tuned DistilBERT models using documented evaluation metrics
The projects create ideal portfolio-ready evidence of PyTorch and NLP modeling practice that you can discuss in technical interviews.
