Almost all cutting-edge AI applications use pretrained models rather than training from scratch, and transfer learning is one of the most useful techniques in contemporary deep learning.
In this course, you'll learn transfer learning from the ground up through clear theoretical explanations and three complete real-world projects.

You'll first build a strong conceptual understanding by learning:

What is Transfer Learning?

Knowledge Base and Knowledge Transfer

Source and Target Domains

Source and Target Tasks

Transfer Learning Workflow

Feature Extraction vs Fine-Tuning

Transfer Learning Terminologies

Types of Transfer Learning

Popular Pretrained Models

Advantages and Disadvantages of Transfer learning

Once you have mastered the theory, you will use these ideas in three real-world projects:


Flower Image Prediction
using MobileNet, ResNet50, and EfficientNetB0 with model comparison and fine-tuning.

SaaS Ticket Routing
using DistilBERT and TF-IDF Vectorization + Logistic Regression to categorize the customer complaints and compares performance with traditional machine learning approach and Transfer learning model.

Video Caption Generation
using faster Whisper for automatic speech-to-text transcription.

You will learn how to create, train, assess, compare, and implement transfer learning models while gaining practical experience with PyTorch throughout the course.

By the end of this course, you'll have both the theoretical knowledge and practical experience needed to confidently implement transfer learning in your own AI projects.

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