TensorFlow Developer Specialization · NLP in TensorFlow

BBC News Classification with Embeddings

A 5-class news classifier that converts raw BBC articles into token sequences, learns embeddings, and predicts article categories.

TextVectorizationStringLookupEmbedding layerGlobalAveragePooling1DNews classification

GitHub

What this project is about

This NLP milestone turns raw BBC articles into integer sequences, learns embeddings, pools the sequence, and predicts one of five news categories.

Implementation focus

The project taught the full text-classification pipeline in TensorFlow: standardization, vectorization, label encoding, batching, embeddings, and validation monitoring.

Key results and artifacts

Dataset: 2,225 articles
Categories: sport, business, politics, tech, entertainment
Validation accuracy: ~91.46% by epoch 8

What I learned

This was the first TensorFlow text pipeline where raw articles became vectors, embeddings, and category probabilities.

Milestone deep dive

What this project proves in my learning path.

A 5-class news classifier that converts raw BBC articles into token sequences, learns embeddings, and predicts article categories. The milestone is included because it shows a complete learning unit: a problem, a data representation, an implementation path, measurable outputs, and a lesson that later connects to larger AI systems.

Input layercourse dataset, features, labels, or raw input artifacts

Model layerimplemented algorithm or neural network pipeline

Evaluation layertests, metrics, plots, or qualitative output

Artifact layermetrics, plots, predictions, and visual evidence

Problem framing

What I was trying to solve

This page is not just a lab description. It explains how I transformed the assignment idea into an engineering story: what the model receives, what it optimizes, what output it should produce, and how the result can be inspected.

Implementation

What I coded or assembled

The implementation focus was the mechanism behind the result: functions, model blocks, training logic, preprocessing, evaluation, and the small technical details that make the notebook work rather than only look correct.

Concepts

Core concepts

TextVectorizationStringLookupEmbedding layerGlobalAveragePooling1DNews classification

These tags show the technical ideas this milestone added to my toolkit.

Learning result

What changed after it

This was the first TensorFlow text pipeline where raw articles became vectors, embeddings, and category probabilities.

Important outputs and evidence

Dataset: 2,225 articles
Categories: sport, business, politics, tech, entertainment
Validation accuracy: ~91.46% by epoch 8

How I would explain it in an interview

I would describe this milestone by starting from the data representation, then explain the model or algorithm, then show what was measured. The important part is not only the final number; it is the ability to reason about why the method works, what assumptions it makes, and what limitations should be improved next.