Phase 1: Text Representation · ~75 minutes · Python

Machine Translation

Translation is the task that paid for NLP research for thirty years and keeps paying now.

Hiring signal: Understanding of machine translation internals

What you will learn

Implement machine translation from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Build Languages: Python Prerequisites: Phase 5 · 10 (Attention Mechanism), Phase 5 · 04 (GloVe, FastText, Subword) Time: ~75 minutes

The Problem

A model reads a sentence in one language and produces a sentence in another. Length varies. Word order varies. Some source words map to multiple target words and vice versa. Idioms refuse one-to-one mapping. "I miss you" in French is "tu me manques" — literally "you are lacking to me." No word-level alignment survives that.

Machine translation is the task that forced NLP to invent encoder-decoders, attention, transformers, and eventually the whole LLM paradigm. Every step forward arrived because translation quality was measurable and the gap between human and machine was stubborn.

This lesson skips the history lesson and teaches the working pipeline of 2026: pretrained multilingual encoder-decoder (NLLB-200 or mBART), subword tokenization, beam search, BLEU and chrF evaluation, and the handful of failure modes that still ship to production uncaught.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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