What This Course Covers?

<text variant="body1">Core NLP Concepts: Word embeddings, tokenization, transformers, attention <text variant="body1">Model Architectures: RNNs, LSTMs, Seq2Seq, Transformers, BERT, GPT <text variant="body1">Translation Systems: From rule-based to neural machine translation <text variant="body1">Speech & Multimodal NLP: ASR, TTS, and vision-language models <text variant="body1">Advanced Topics: RLHF, low-resource NLP, explainability, model compression <text variant="body1">Practical Demos: Real-time applications, chatbot frameworks, translation assistants

<text variant="body1">By the end of this course, you’ll be able to: <text variant="body1">Build NLP models using transformers, BERT, GPT, etc. <text variant="body1">Fine-tune pre-trained models for translation, chatbots, and classification <text variant="body1">Apply subword tokenization techniques and manage OOV tokens <text variant="body1">Design custom machine translation pipelines <text variant="body1">Implement speech recognition and TTS applications <text variant="body1">Explore multimodal learning (text + image + speech) <text variant="body1">Evaluate NLP systems using metrics like BLEU, ROUGE <text variant="body1">Understand and apply RLHF and Ethical AI principles

<text variant="body1">Natural Language Understanding (NLU) <text variant="body1">Machine Translation <text variant="body1">Speech-to-Text and Text-to-Speech <text variant="body1">Multimodal NLP Integration <text variant="body1">Model Fine-Tuning & Transfer Learning <text variant="body1">Attention Mechanisms & Transformers <text variant="body1">Chatbot Development <text variant="body1">Model Evaluation and Deployment

What tools or platforms will be used?

<text variant="body1">Google Colab (for hands-on demos), Hugging Face, PyTorch, and popular libraries like transformers and datasets.

Are projects or assignments included?

<text variant="body1">Yes, practical demonstrations and mini-projects are provided across modules to ensure hands-on experience.

How can I build a chatbot using this course?

<text variant="body1">The course includes a full section on chatbot frameworks. You’ll learn to: <text variant="body1">Understand intents and entities <text variant="body1">Use pretrained models like BERT for NLU <text variant="body1">Fine-tune transformers for conversational AI <text variant="body1">Integrate chatbots with business workflows A complete demonstration walks you through building a context-aware chatbot.

Will I learn to make a text-to-speech (TTS) model?

<text variant="body1">Yes! The Speech Processing module covers TTS systems and includes a demonstration on building a voice narration assistant using models like Tacotron or Google TTS APIs.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Neural Models and Machine Translation

This course is part of Mastering NLP: Tokenization, Sentiment Analysis & Neural MT Specialization

Instructor: Edureka

Included with

Learn more

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

What You Will LearnBuild neural NLP models using RNNs, LSTMs, GRUs, and transformers for contextual text understanding and sequence-based tasks.
Apply attention mechanisms and encoder-decoder architectures to design effective machine translation and language generation systems.
Fine-tune pretrained models like BERT, RoBERTa, and MarianMT to perform multilingual NLP tasks with domain-specific accuracy.
Evaluate translation and classification systems using BLEU, ROUGE, and semantic similarity to improve performance and reliability.

Skills you'll gain

Tools you'll learn

PyTorch (Machine Learning Library)

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

18 assignments¹

AI Graded see disclaimer

Taught in English

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Build your subject-matter expertise

This course is part of the Mastering NLP: Tokenization, Sentiment Analysis & Neural MT Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

This course guides you through the core concepts behind neural language models and machine translation, focusing on how RNNs, attention, and transformers enable powerful NLP applications used in today’s AI systems.

Through hands-on exercises, you’ll learn to build, fine-tune, and evaluate neural models for contextual language understanding, sentiment classification, and multilingual translation across various domains. By the end of this course, you will be able to: - Explain and implement core neural architectures, including RNNs, LSTMs, GRUs, and Transformers - Apply encoder-decoder frameworks and attention mechanisms to build translation systems - Fine-tune pretrained models like BERT, RoBERTa, and MarianMT for contextual NLP tasks - Address challenges such as domain adaptation, low-resource translation, and error correction - Evaluate model performance using BLEU, ROUGE, and semantic similarity metrics This course is ideal for NLP practitioners, machine learning engineers, and researchers aiming to build high-performing neural NLP systems for translation, classification, and conversational AI. A working knowledge of Python, NLP concepts, and machine learning is recommended. Join us to master the neural foundations driving next-generation language understanding and generation.

Explore the foundations of neural networks in NLP, from word embeddings and RNNs to the powerful Transformer architecture. Learn how pretraining and fine-tuning power today’s intelligent systems through theory and hands-on demonstrations.

What's included

22 videos5 readings5 assignments1 discussion prompt

22 videos Total 103 minutes

Specialization Introduction 5 minutes
Course Introduction 4 minutes
Word Embeddings and Their Role in NLP 5 minutes
RNNs and LSTMs in NLP Applications 6 minutes
Sequence-to-Sequence (Seq2Seq) Models 3 minutes
Introduction to Transformers 4 minutes
Pretraining & Fine-Tuning Paradigm (GPT-4, T5x, BART) 5 minutes
Demonstration: Intelligent Query Response System for Customer Support (Word Embedding and Query Classification) 6 minutes
Demonstration: Intelligent Query Response System for Customer Support (Seq2Seq Model and Fine Tuning T5 Model) 7 minutes
Why Attention is Important in NLP 4 minutes
Self-Attention in Transformers 4 minutes
Multi-Head Attention in BERT 5 minutes
Visualizing Attention Mechanisms 4 minutes
Subword Tokenization (WordPiece, SentencePiece, Unigram Models) 6 minutes
Byte-Pair Encoding (BPE) in Transformers 6 minutes
Tokenization Pipelines in Hugging Face 4 minutes
Handling Out-of-Vocabulary (OOV) Words in Pretrained Models 4 minutes
Demonstration: Tokenization of Low-resource Language 5 minutes
BERT: Fine-Tuning and Real-World Applications 6 minutes
GPT Models: Understanding and Use Cases 4 minutes
Fine-Tuning Transformers for NLP Tasks 4 minutes
Transfer Learning for NLP 3 minutes

5 readings Total 65 minutes

Neural Architectures and Translation Challenges in Modern NLP 15 minutes
Attention Mechanisms in NLP: Understanding What Matters 15 minutes
Tokenization Techniques for Pretrained Language Models 15 minutes
Transformers and Pretrained Models in NLP 15 minutes
Module Summary: Neural Language Models 5 minutes

5 assignments Total 54 minutes

Practice Quiz: Neural Networks for NLP 6 minutes
Practice Quiz: Attention Mechanisms in NLP 6 minutes
Practice Quiz: Tokenization for Pretrained Models 6 minutes
Practice Quiz: Transformers and Pretrained Models 6 minutes
Knowledge Check: Neural Language Models 30 minutes

1 discussion prompt Total 10 minutes

Introduce Yourself 10 minutes

Understand the evolution of machine translation from rule-based systems to cutting-edge neural and transformer-based models. Dive into multilingual strategies, error handling, and domain adaptation for real-world translation challenges.

What's included

21 videos5 readings5 assignments

21 videos Total 112 minutes

Evolution of Machine Translation: Rule-Based to Neural MT 5 minutes
Challenges in Machine Translation (Data Sparsity, Morphological Complexity) 7 minutes
Evaluation Metrics (BLEU Score, ROUGE, METEOR) 5 minutes
Unsupervised Machine Translation 6 minutes
Multilingual Pretraining Strategies for Zero-Shot Translation 6 minutes
What is Neural Machine Translation (NMT)? 6 minutes
Encoder-Decoder Architecture in NMT 5 minutes
Using LSTMs for Sequence Modeling in Translation 6 minutes
Attention Mechanisms in NMT 5 minutes
Training an NMT Model 4 minutes
Demonstration: Using LSTMS for Sequence Modeling in Translation 6 minutes
How Transformers Revolutionized MT (BERT, MarianMT) 5 minutes
Multilingual NLP and Zero-Shot Translation 4 minutes
Low-Resource Language Translation Techniques 5 minutes
Zero-Shot Translation Techniques 4 minutes
Demonstration: Zero-Shot Translation for Low Resource languages 7 minutes
Types of Errors (Lexical, Syntactic, Semantic, Pragmatic) 4 minutes
Detecting and Fixing Common Translation Errors 7 minutes
Customizing Translation Pipelines for Domain-Specific Needs 5 minutes
Reinforcement Learning from Human Feedback (RLHF) for Translation Fine-Tuning 6 minutes
Demonstration: Error detection in English to French Translation 6 minutes

5 readings Total 75 minutes

Introduction to Machine Translation 20 minutes
Neural Machine Translation (NMT) 15 minutes
Transformer-Based Machine Translation 15 minutes
Error Handling and Custom Translation Pipelines 15 minutes
Module Summary: Machine Translation (MT) 10 minutes

5 assignments Total 54 minutes

Practice Quiz: Introduction to Machine Translation 6 minutes
Practice Quiz: Neural Machine Translation (NMT) 6 minutes
Practice Quiz: Transformer-Based Machine Translation 6 minutes
Practice Quiz: Error Handling and Custom Translation Pipelines 6 minutes
Knowledge Check: Machine Translation (MT) 30 minutes

Discover how speech and multimodal data shape modern NLP. This module covers speech-to-text, TTS, and the integration of vision and audio with text for richer AI applications, alongside key trends like real-time NLP and model efficiency.

What's included

14 videos4 readings4 assignments

14 videos Total 69 minutes

Fundamentals of Speech Recognition 4 minutes
Automatic Speech Recognition (ASR) Models (e.g., Whisper, DeepSpeech) 6 minutes
Challenges in Speech Processing 4 minutes
TTS Applications in NLP 4 minutes
Demonstration: Building a Voice Narration Assistant 5 minutes
Introduction to Multimodal Learning 4 minutes
Vision-Language Models (CLIP, BLIP) 5 minutes
Integrating Text and Speech in NLP Applications 4 minutes
Generative Models for Multimodal Content 5 minutes
Model Compression (Quantization, Pruning, Knowledge Distillation) 6 minutes
Real-Time NLP for Edge Devices 5 minutes
Explainability in Deep NLP Models 4 minutes
Ethical AI in NLP 6 minutes
Emerging Trends (GPT-5, HyperNetworks, Autonomous AI Agents) 6 minutes

4 readings Total 95 minutes

Understanding Speech-to-Text and Text-to-Speech: Foundations of Speech Processing in NLP 30 minutes
Exploring Multimodal NLP: Integrating Text, Vision, and Speech for Richer AI Understanding 20 minutes
Future Directions in NLP: Model Efficiency, Ethics, and Emerging Trends 15 minutes
Module Summary: Speech and Multimodal NLP 30 minutes

4 assignments Total 48 minutes

Practice Quiz: Speech-to-Text and Text-to-Speech (TTS) 6 minutes
Practice Quiz: Multimodal NLP (Text + Images + Audio) 6 minutes
Practice Quiz: Future Trends in NLP and Model Efficiency 6 minutes
Knowledge Check: Speech and Multimodal NLP 30 minutes

Learn how to build intelligent chatbots using NLP techniques. This module covers intent detection, entity extraction, contextual fine-tuning, and performance evaluation, preparing you to design chatbots that integrate seamlessly into business workflows.

What's included

6 videos2 readings2 assignments

6 videos Total 30 minutes

NLP for Chatbot Frameworks 5 minutes
Understanding Intents and Entities in Chatbots 3 minutes
Chatbot Integration with Business Workflows 4 minutes
Fine-Tuning Models for Contextual Chatbots 7 minutes
Evaluating Chatbot Performance (Accuracy, Coherence, User Feedback) 4 minutes
Demonstration: Building Multiturn Chatbots 7 minutes

2 readings Total 25 minutes

Understanding Chatbots: NLP, Intents, Entities,Integration, and Contextual Fine-Tuning 15 minutes
Module Summary: Building Chatbots 10 minutes

2 assignments Total 36 minutes

Practice Quiz: Building Chatbots 6 minutes
Knowledge Check: Building Chatbots 30 minutes

Conclude the course by reviewing key concepts across neural models and machine translation. This module includes a graded knowledge check, a comprehensive course summary, and a project focused on building a smart multilingual assistant for global applications.

What's included

1 video1 reading2 assignments1 discussion prompt2 ungraded labs

1 video Total 3 minutes

Course Summary: Neural Models and Machine Translation 3 minutes

1 reading Total 60 minutes

Final Practice Project: Smart Multilingual Assistant for Global E-Commerce 60 minutes

2 assignments Total 60 minutes

End Course Knowledge Check: Neural Models and Machine Translation 30 minutes
Designing a Neural Multilingual Assistant 30 minutes

1 discussion prompt Total 10 minutes

Describe your Learning Journey 10 minutes

2 ungraded labs Total 120 minutes

Attention in NLP 60 minutes
Understanding NMT with Attention and BLEU Evaluation 60 minutes

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Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Edureka

157 Courses 146,599 learners

Offered by

Edureka

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Frequently asked questions

Neural networks, which can handle very huge datasets and require little supervision, are used in neural machine translation to convert source text to target text.

NMT is a machine translation technique that translates text using artificial neural networks. In contrast to previous statistical techniques, it uses a single, integrated neural network to train to translate straight from source to destination language.

Through the analysis of enormous volumes of parallel text data, this network learns to map input sentences to output translations.

This course offers the most comprehensive and up-to-date learning path in NLP, covering everything from foundational concepts to cutting-edge trends like GPT-4, Multimodal Models, and Ethical AI. Whether you're an aspiring ML engineer or an NLP practitioner, this course prepares you to build, fine-tune, and deploy real-world NLP systems with confidence.

Core NLP Concepts: Word embeddings, tokenization, transformers, attention

Model Architectures: RNNs, LSTMs, Seq2Seq, Transformers, BERT, GPT

Translation Systems: From rule-based to neural machine translation

Speech & Multimodal NLP: ASR, TTS, and vision-language models

Advanced Topics: RLHF, low-resource NLP, explainability, model compression

Practical Demos: Real-time applications, chatbot frameworks, translation assistants

By the end of this course, you’ll be able to:

Build NLP models using transformers, BERT, GPT, etc.

Fine-tune pre-trained models for translation, chatbots, and classification

Apply subword tokenization techniques and manage OOV tokens

Design custom machine translation pipelines

Implement speech recognition and TTS applications

Explore multimodal learning (text + image + speech)

Evaluate NLP systems using metrics like BLEU, ROUGE

Understand and apply RLHF and Ethical AI principles

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

Neural Models and Machine Translation

Neural Models and Machine Translation

What you'll learn

Skills you'll gain

Tools you'll learn

Details to know

See how employees at top companies are mastering in-demand skills

Build your subject-matter expertise

There are 5 modules in this course

Neural Language Models

What's included

Machine Translation (MT)

What's included

Speech and Multimodal NLP

What's included

Building Chatbots

What's included

Course Wrap-up and Assessments

What's included

Earn a career certificate

Instructor

Offered by

Explore more from Machine Learning

Generative AI Language Modeling with Transformers

Sequence Modeling, Transformers, and Transfer Learning

Natural Language Processing - Deep Learning Models in Python

Introduction to Transformer Models for NLP: Unit 1

Why people choose Coursera for their career

Felipe M.

Jennifer J.

Larry W.

Chaitanya A.

Open new doors with Coursera Plus

Advance your career with an online degree

Join over 3,400 global companies that choose Coursera for Business

Frequently asked questions

What is the process of neural machine translation?

Neural Machine Translation: What Is It?

Why this Course?

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