Python Tutorial: Introduction to machine translation

Want to learn more? Take the full course at https://www.datacamp.com/courses/machine-translation-in-python?embedded=true&slug=machine-translation-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Bonjour! Hallo!, I am Thushan Ganegedara and in this course you will learn to implement machine translation models using the popular deep learning library Keras. Ability to communicate in foreign languages helps us in many instances, such as when traveling overseas. Machine translation services such as Google translation service, can help you to understand hundreds of languages at the press of a button. In this course, you will be learning the inner workings of the models that are empowering these services. In chapter 1, you will be introduced to machine translation and the encoder-decoder architecture, which is a common deep learning architecture used for machine translation models. Next, in chapter 2, you will be implementing an encoder-decoder model using the Keras functional API. In chapter 3, you will learn how to train a model and generate translations using the trained model. Finally, you will learn and implement several techniques that improve the performance of machine translation models such as Teacher Forcing. The dataset that you'll be using in this course consists of two text files. One file contains a set of English sentences, where each line in the file contains a single sentence. And the other file contains the corresponding French translations of the English sentences. Here, you can see an example of a machine translation task. We want to translate the English sentence "I like cats" to French. In machine translation terminology, the English language, the language of the sentence to be translated, is called the source language. The French language, the language of the translated sentence, is called the target language. Let's now see how a machine translation model can