Research and Data Analysis

Transfer Learning and Fine-tuning Pre-trained Models Training Course

Introduction

In today's fast-paced AI and machine learning landscape, Transfer Learning and fine-tuning of pre-trained models are game-changing strategies for accelerating model development, reducing training time, and improving performance on specialized tasks. By leveraging large, generalized pre-trained models like BERT, GPT, ResNet, or Vision Transformers, organizations can tap into vast knowledge encoded from massive datasets and repurpose it for their specific domains—be it healthcare, finance, retail, NLP, or computer vision.

Transfer Learning and Fine-tuning Pre-trained Models Training Course provides a deep dive into the core principles, frameworks, and applications of transfer learning, from zero-shot learning to domain adaptation, and enables participants to gain hands-on experience with TensorFlow, PyTorch, Hugging Face Transformers, and more. Through real-world case studies, practical assignments, and collaborative learning, participants will acquire the essential skills to build efficient, scalable, and intelligent AI models with minimal labeled data and maximum impact.

Programme Curriculum

Transfer Learning and Fine-tuning Pre-trained Models Training Course

Introduction

Course Objectives

Understand the fundamentals of transfer learning and domain adaptation
Explore various types of pre-trained models across NLP and CV
Implement fine-tuning strategies for custom datasets
Apply zero-shot and few-shot learning techniques
Gain proficiency with TensorFlow Hub, PyTorch Hub, and Hugging Face
Learn to handle overfitting and catastrophic forgetting
Integrate transfer learning with real-time AI pipelines
Evaluate performance using cross-domain metrics
Optimize models for low-resource environments
Understand multi-task and continual learning paradigms
Build scalable solutions with MLOps and AutoML tools
Explore ethical considerations and model bias in transfer learning
Work through industry-relevant case studies and hands-on labs

Target Audience

Data Scientists
Machine Learning Engineers
AI Researchers
Software Developers
NLP Practitioners
Computer Vision Engineers
Academic Researchers
Tech Startups and Innovators

Course Duration: 5 days

Course Modules

Module 1: Foundations of Transfer Learning

Definition and evolution of transfer learning
Core principles: feature reuse, knowledge distillation
Categories: inductive, transductive, unsupervised
Comparison with traditional ML
Industry use cases: NLP, CV, speech
Case Study: Transfer learning for pneumonia detection using chest X-rays

Module 2: Pre-trained Models in NLP and Vision

Overview of models: BERT, GPT, T5, ResNet, VGG, ViT
Tokenization and embeddings
Transferability and task generalization
Choosing the right model
Model architecture dissection
Case Study: Fine-tuning BERT for legal document classification

Module 3: Fine-tuning Techniques and Strategies

Layer freezing and unfreezing
Discriminative learning rates
Gradual unfreezing and slanted triangular learning
Domain-specific fine-tuning
Monitoring model drift
Case Study: Custom object detection using YOLOv5

Module 4: Frameworks and Tools for Implementation

TensorFlow Hub & PyTorch Hub
Hugging Face Transformers library
Training scripts and model APIs
Using Google Colab and Kaggle notebooks
Model optimization and saving checkpoints
Case Study: Sentiment analysis using Hugging Face pipeline

Module 5: Advanced Topics in Transfer Learning

Few-shot and zero-shot learning
Meta-learning and multi-task learning
Continual learning and knowledge transfer
Dealing with domain shift
Cross-lingual and multi-modal models
Case Study: GPT-3 for multilingual summarization

Module 6: Evaluation and Performance Tuning

Domain-specific metrics: BLEU, F1, mAP
Handling imbalanced datasets
Avoiding overfitting and bias
Explainability in transferred models
Visualization with Grad-CAM and SHAP
Case Study: Model evaluation in a multi-domain e-commerce dataset

Module 7: Deployment and Real-time Inference

Converting models for ONNX and TFLite
Serverless deployment with AWS Lambda or Google Cloud Functions
Using Docker and FastAPI
Latency and throughput optimization
Integration with mobile/edge devices
Case Study: Real-time object tracking with fine-tuned MobileNet

Module 8: Ethics, Security, and Sustainability

Ethical AI and bias in pre-trained models
Secure deployment and adversarial threats
Privacy-preserving fine-tuning techniques
Carbon footprint of large-scale models
Governance and compliance issues
Case Study: Auditing GPT fine-tuning for hate speech detection

Training Methodology

Interactive instructor-led sessions
Real-world case studies with code walkthroughs
Hands-on labs and assignments using cloud-based tools
Peer collaboration via discussion forums and group projects
Capstone project and certificate of completion

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Transfer learning model adaptation deep learning

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000