You will be able to select a suitable pretrained model, prepare quality datasets, and run supervised and instruction fine-tuning workflows using PyTorch, Hugging Face, and LoRA. You will also be able to evaluate models for accuracy and fairness, then serve and monitor them using FastAPI and Docker.

This program is ideal for developers, data scientists, and machine learning engineers who want to move beyond training models from scratch and customize pretrained models for specific business tasks. It is well-suited for learners interested in applied deep learning and hands-on model development.

Yes. Upon successful completion of all courses and required assessments, learners will receive a shareable certificate recognizing their skills in transfer learning, fine-tuning, and AI model deployment.

Learners will build projects such as a neural network implemented from scratch in NumPy, a self-attention mechanism coded by hand, an end-to-end instruction fine-tuning workflow with LoRA, and a containerized inference service. These projects emphasize real-world problem solving and practical model development.

You will work with modern machine learning tools including Python, PyTorch, Hugging Face Transformers, NumPy, Pandas, Scikit-learn, LoRA, Fairlearn, FastAPI, Docker, and more. You will also use Jupyter and VS Code as your development environment throughout the hands-on labs.

You will gain skills in transfer learning, dataset preparation, tokenization, supervised and instruction fine-tuning, hyperparameter tuning, parameter-efficient methods such as LoRA, model evaluation, bias auditing, and model serving. These skills apply directly to building and maintaining AI models in real production environments.

The specialization equips learners with practical, in-demand skills for customizing and deploying AI models, helping them transition into machine learning engineering and applied AI-focused roles. Fine-tuning and model deployment are among the most sought-after capabilities in current AI hiring.

This course is completely online, so there's no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Fine-tuning is a transfer learning technique in which an already trained (pretrained) model is further trained on a new, task-specific dataset. This allows the model to adapt its existing knowledge to perform more accurately on a specific task without requiring training from scratch.

Pretrained models are AI models that have already been trained on large datasets to learn patterns, language, or visual features. Instead of training a model from scratch, you can reuse these models and adapt them to new tasks through transfer learning and fine-tuning, saving both time and computing resources.

LoRA (Low-Rank Adaptation) is an AI technique used to customize and fine-tune large machine learning models, such as large language models (LLMs) and image generation models.

It enables efficient fine-tuning by updating only a small number of model parameters, reducing computing requirements while maintaining strong performance. This makes LoRA a practical and cost-effective approach for adapting pretrained models to specific tasks. 

Hugging Face is the leading open-source platform and community for artificial intelligence and machine learning. In this Specialization, you will use Hugging Face with PyTorch to work with pretrained models, fine-tune them for specific tasks, and build real-world AI applications. 

Transfer learning and fine-tuning enables you to build accurate AI models without requiring massive datasets, billions of parameters, or expensive computing resources. 

These techniques are widely used to customize pretrained models for real-world applications, making them valuable skills for modern machine learning and AI development.

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.