Machine Translation (Coursera)

Machine Translation (Coursera)

Welcome to the CLICS-Machine Translation MOOC. This MOOC explains the basic principles of machine translation. Machine translation is the task of translating from one natural language to another natural language. Therefore, these algorithms can help people communicate in different languages. Such algorithms are used in common applications, from Google Translate to apps on your mobile device.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

After taking this course you will be able to understand the main difficulties of translating natural languages and the principles of different machine translation approaches. A main focus of the course will be the current state-of-the-art neural machine translation technology which uses deep learning methods to model the translation process. You will be able to decide which concepts fit your machine translation application best.

Syllabus

WEEK 1: Introduction to the basics of Machine Translation
WEEK 2: Language
WEEK 3: Evaluation
WEEK 4: Statistical Machine Translation
WEEK 5: Neural Network Models
WEEK 6: NMT
WEEK 7: More NMT

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Practical Machine Learning on H2O (Coursera) Coursera
H2O.ai

Practical Machine Learning on H2O (Coursera)

In this course, we will learn all the core techniques needed to make effective use of H2O. Even if you have no prior experience of machine learning, even if your math is weak, by the end of this course you will be able to make machine learning models using a variety of algorithms. We will be using linear models, random forest, GBMs and of course deep learning, as well as some unsupervised learning algorithms.

Nov 9th 2026
5-12 Weeks
Advanced Linear Models for Data Science 1: Least Squares (Coursera) Coursera
Johns Hopkins University

Advanced Linear Models for Data Science 1: Least Squares (Coursera)

Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: a basic understanding of linear algebra and multivariate calculus; a basic understanding of statistics and regression models; at least a little familiarity with proof based mathematics; basic knowledge of the R programming language.

Nov 16th 2026
5-12 Weeks
Attention Mechanism (Coursera) Coursera
Google Cloud

Attention Mechanism (Coursera)

This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering.

Nov 16th 2026
1 Week
ML Pipelines on Google Cloud (Coursera) Coursera
Google Cloud

ML Pipelines on Google Cloud (Coursera)

In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata.

Nov 9th 2026
4 Weeks
Machine Learning: Concepts and Applications (Coursera) Coursera
University of Chicago

Machine Learning: Concepts and Applications (Coursera)

This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning.

Nov 16th 2026
5-12 Weeks
AI-Driven Attribution Testing (Coursera) Coursera
Board Infinity

AI-Driven Attribution Testing (Coursera)

Welcome to AI-Driven Attribution Testing course an engaging and comprehensive course designed to guide you through the fundamental concepts and practical applications of attribution testing powered by artificial intelligence. This course is most suitable for marketers, data analysts, data scientists, and business leaders who aim to leverage data-driven insights for decision-making. It's also beneficial for students and professionals with a keen interest in the convergence of AI, data analysis, and marketing.

Nov 16th 2026
2 Weeks
Cadeia de Suprimentos na Nuvem (Coursera) Coursera
FIA Business School

Cadeia de Suprimentos na Nuvem (Coursera)

Nossas boas-vindas ao Curso Cadeia de Suprimentos na Nuvem. Neste curso, você aprenderá como o supply chain pode ampliar o valor da empresa explorando as diversas ferramentas disponíveis em cloud para potencializar a visibilidade e a responsividade da cadeia, melhorando o nível de serviço prestado aos clientes.

Nov 9th 2026
5-12 Weeks
Using R for Regression and Machine Learning in Investment (Coursera) Coursera
Sungkyunkwan University - SKKU

Using R for Regression and Machine Learning in Investment (Coursera)

In this course, the instructor will discuss various uses of regression in investment problems, and she will extend the discussion to logistic, Lasso, and Ridge regressions. At the same time, the instructor will introduce various concepts of machine learning. You can consider this course as the first step toward using machine learning methodologies in solving investment problems. The course will cover investment analysis topics, but at the same time, make you practice it using R programming. This course's focus is to train you to use various regression methodologies for investment management that you might need to do in your job every day and make you ready for more advanced topics in machine learning.

Nov 16th 2026
2 Weeks
计算机辅助翻译原理与实践 Principles and Practice of Computer-Aided Translation (Coursera) Coursera
Peking University

计算机辅助翻译原理与实践 Principles and Practice of Computer-Aided Translation (Coursera)

讲授计算机辅助翻译技术的基本概念,及多种辅助翻译工具的原理和使用方法,锻炼学生在技术环境下从事翻译工作等其他各类语言服务工作的能力,并帮助学生理解信息化时代的语言服务工作。 This course introduces the basic concepts of computer-aided translation technology and helps students learn to use computer-aided translation tools.

Nov 9th 2026
13-24 Weeks
Deep Learning for Business (Coursera) Coursera
Yonsei University

Deep Learning for Business (Coursera)

Your smartphone, smartwatch, and automobile (if it is a newer model) have AI (Artificial Intelligence) inside serving you every day. In the near future, more advanced “self-learning” capable DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of your business and industry. So now is the right time to learn what DL and ML is and how to use it in advantage of your company. This course has three parts, where the first part focuses on DL and ML technology based future business strategy including details on new state-of-the-art products/services and open source DL software, which are the future enablers.

Nov 16th 2026
5-12 Weeks