Course Summary
This course provides an introduction to machine translation, covering the basic techniques and models used to translate text from one language to another. You will learn how to build a machine translation system using neural networks and evaluate its performance.Key Learning Points
- Learn the basic techniques and models used in machine translation
- Build a machine translation system using neural networks
- Evaluate the performance of your machine translation system
Job Positions & Salaries of people who have taken this course might have
- Machine Translation Engineer
- USA: $94,000
- India: ₹1,329,000
- Spain: €36,000
- Machine Learning Engineer
- USA: $112,000
- India: ₹1,565,000
- Spain: €40,000
- Data Scientist
- USA: $120,000
- India: ₹1,684,000
- Spain: €50,000
Related Topics for further study
Learning Outcomes
- Understand the basic techniques and models used in machine translation
- Build and evaluate a machine translation system using neural networks
- Apply machine translation to real-world problems
Prerequisites or good to have knowledge before taking this course
- Basic understanding of programming concepts
- Familiarity with Python programming language
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures
- Quizzes and assignments
Similar Courses
- Natural Language Processing with Deep Learning
- Applied Data Science with Python
Related Education Paths
Notable People in This Field
- Andrew Ng
- Fei-Fei Li
Related Books
Description
Welcome to the CLICS-Machine Translation MOOC
Outline
- Introduction to the basics of Machine Translation
- Introduction
- History
- Approaches
- Applications
- Overview
- Additional materials
- History
- Introduction
- Language
- Language
- Difficulties
- Approaches to MT
- Data
- Additional materials
- Additional materials
- Additional materials
- Additional materials
- Language
- Difficulties
- Approaches
- Data
- Evaluation
- Evaluation
- Human Evaluation
- BLEU
- MT Metrics
- Additional materials
- Additional materials
- Additional materials
- Additional materials
- Evaluation
- Human Evaluation
- BLEU
- Machine Translation Metrics
- Statistical Machine Translation
- Word-based Statistical Machine Translation
- Language Model
- From Noisy channel model to Log-linear Model
- Phrase based Machine Translation
- Advanced SMT
- Additional materials
- Additional materials
- Additional materials
- Additional materials
- Word-based Statistical Machine Translation
- Language Model
- From Noisy-channel model to Log-linear model
- Advanced SMT
- Neural Network Models
- Introduction to Neutral Networks
- Feed Foward Neural Network Language Model
- Reccurent Neural Network Language Model
- Neural Translation Model
- Additional materials
- Additional materials
- Additional materials
- Additional materials
- Introduction to Neural Networks
- Feed Forward Neural Network Language Model
- Recurrent Neural Network Language Model
- Neural Translation Model
- NMT
- Encoder Decoder
- NMT Training
- NMT Decoding
- Attention Based NMT
- Combination
- Additional materials
- Additional materials
- Additional materials
- Additional materials
- Encoder-Decoder
- NMT-Training
- NMT Decoding
- Attention based NMT
- Combination
- More NMT
- Vocabulary
- Monolingual Data
- Multilingual Machine Translation
- Architectures
- Additional materials
- Additional materials
- Additional materials
- Additional materials
- Vocabulary
- Monolingual Data
- Multilingual
- NMT - Architectures
Summary of User Reviews
Learn the fundamentals of machine translation with this comprehensive course on Coursera. Many users found the course to be well-structured, with a great balance of theory and practical application.Key Aspect Users Liked About This Course
Practical application of machine translation techniquesPros from User Reviews
- Excellent course structure
- Great balance of theory and practical application
- In-depth coverage of machine translation techniques
- Engaging and knowledgeable instructors
- Access to additional resources and materials
Cons from User Reviews
- Some users found the course material to be too technical
- Limited interaction with instructors and peers
- Assignments and quizzes can be challenging
- Not suitable for beginners
- Some users reported technical issues with the platform
Alexander Waibel
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