Machine Translation

Learn how neural machine translation works and build multilingual applications. From using translation APIs like Google Translate and DeepL to fine-tuning open-source models like MarianMT and NLLB-200, this course covers everything you need to translate text at scale.

Start Course → Jump to APIs
6
Lessons
35+
Examples
~2hr
Total Time
🌐
Multilingual

What You'll Learn

By the end of this course, you will understand how NMT works and be able to build, evaluate, and deploy translation systems.

📚

NMT Fundamentals

Understand encoder-decoder architectures, attention mechanisms, and how transformers revolutionized machine translation.

☁

Translation APIs

Integrate Google Translate, DeepL, and Azure Translator into your applications with practical Python examples.

🛠

Fine-Tuning Models

Fine-tune MarianMT and NLLB-200 on domain-specific data to improve translation quality for specialized content.

📊

Evaluation

Measure translation quality with BLEU, chrF, and COMET scores. Learn human evaluation strategies.

Course Lessons

Follow the lessons in order or jump to any topic you need.

Beginner

1. Introduction

What is machine translation? Explore its history from rule-based systems to neural models, and understand key concepts.

10 min read →
 Intermediate

2. How NMT Works

Dive into encoder-decoder architectures, attention mechanisms, transformers, and subword tokenization for translation.

15 min read →
 Beginner

3. Translation APIs

Use Google Translate API, DeepL API, and Azure Translator to build translation features into your applications.

15 min read →
 Advanced

4. Fine-Tuning

Fine-tune MarianMT and NLLB-200 on custom parallel corpora for domain-specific translation using Hugging Face.

20 min read →
 Intermediate

5. Evaluation

Measure translation quality with BLEU, chrF, COMET, and human evaluation. Understand their strengths and limitations.

15 min read →
 Intermediate

6. Best Practices

Production deployment strategies, handling edge cases, cost optimization, and building multilingual applications.

10 min read →

Prerequisites

What you need before starting this course.

Before You Begin:
  • Basic Python programming knowledge
  • Familiarity with NLP concepts (helpful but not required)
  • Python 3.8+ installed with pip
  • API keys for Google Cloud, DeepL, or Azure (free tiers available)