Machine Translation in Python

Are you curious about the inner workings of the models that are behind products like Google Translate?
Start Course for Free
4 Hours16 Videos58 Exercises2,379 Learners
4950 XP

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies

Course Description

The need to pack a bilingual dictionary for your European holiday or keeping one on your desk to complete your foreign language homework is a thing of the past. You just hop on the internet and make use of a language translation service to quickly understand what the street sign means or finding out how to greet and thank a foreigner in their language. Behind the language translation services are complex machine translation models. Have you ever wondered how these models work? This course will allow you to explore the inner workings of a machine translation model. You will use Keras, a powerful Python-based deep learning library, to implement a translation model. You will then train the model to perform an English to French translation, and you will be shown techniques to improve your model. At the end of this course, you would have developed an in-depth understanding of machine translation models and appreciate them even more!

  1. 1

    Introduction to machine translation

    In this chapter, you'll understand what the encoder-decoder architecture is and how it is used for machine translation. You will also learn about Gated Recurrent Units (GRUs) and how they are used in the encoder-decoder architecture.
    Play Chapter Now
  2. 2

    Implementing an encoder decoder model with Keras

    In this chapter, you will implement the encoder-decoder model with the Keras functional API. While doing so, you will learn several useful Keras layers such as RepeatVector and TimeDistributed layers.
    Play Chapter Now
  3. 3

    Training and generating translations

    In this chapter, you will train the previously defined model and then use a well-trained model to generate translations. You will see that our model does a good job when translating sentences.
    Play Chapter Now
  4. 4

    Teacher Forcing and word embeddings

    In this chapter, you will learn about a technique known as Teacher Forcing, which enables translation models to be trained better and faster. Then you will learn how you can use word embeddings to make the model even better.
    Play Chapter Now
In the following tracks
Deep Learning for NLP
Ruanne Van Der WaltMona Khalil
Thushan Ganegedara Headshot

Thushan Ganegedara

Data Scientist and Author
Thushan Ganegedara is a Senior Data Scientist. He is the author of TF2 in Action - Manning and NLP with TensorFlow (v1.6). He has over 4 years experience with TensorFlow. Thushan likes to wear many hats as a YouTuber, blogger, presenter and a StackOverflow contributor. Deep learning and machine learning stand out as his passions. Unless he's dwelling in latest ML research you can find him meditating or swimming (not at the same time). Follow him on LinkedIn.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA