Building Vision and NLP Workflows with TensorFlow Pipelines

Building Vision and NLP Workflows with TensorFlow and Transformers focuses on developing machine learning pipelines for computer vision and natural language processing tasks. In this course, you will learn how modern AI applications process images and text using deep learning frameworks and transformer architectures. You will begin by building computer vision pipelines that train and evaluate models for image classification and related tasks. Next, you will construct natural language processing workflows using transformer-based architectures to process and analyze text data. The course also explores how tokenization, embeddings, and model evaluation techniques improve NLP model performance. In the final modules, you will use TensorFlow and Keras to build end-to-end machine learning workflows, from data preparation to optimized model deployment. By the end of the course, you will be able to design scalable AI pipelines that handle image and language data, evaluate model performance using appropriate metrics, and optimize machine learning workflows for real-world applications. Tools used in this course include Python, TensorFlow, Keras, and transformer-based NLP frameworks

• Build computer vision pipelines for deep learning model training and evaluation
• Develop NLP workflows using transformer architectures for text processing
• Implement end-to-end machine learning pipelines with TensorFlow and Keras
• Evaluate model performance using task-specific metrics and error analysis

• Basic familiarity with the topic and common terminology
• Readiness to practice through applied exercises or case-based work