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

Développez vos compétences avec Coursera Plus pour 239 $/an (habituellement 399 $). Économisez maintenant.

Ce cours n'est pas disponible en Français (France)

Nous sommes actuellement en train de le traduire dans plus de langues. Consultez les langues disponibles.

Neural Models and Machine Translation

Ce cours fait partie de Spécialisation "Mastering NLP: Tokenization, Sentiment Analysis & Neural MT "

Instructeur : Edureka

Inclus avec

5 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

5 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

Catégorie : Artificial Intelligence and Machine Learning (AI/ML)
Catégorie : Deep Learning
Catégorie : Large Language Modeling
Catégorie : Model Evaluation
Catégorie : Machine Learning
Catégorie : Artificial Neural Networks
Catégorie : Transfer Learning
Catégorie : Natural Language Processing
Catégorie : Recurrent Neural Networks (RNNs)

Outils que vous découvrirez

Catégorie : PyTorch (Machine Learning Library)

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Évaluations

18 affectations¹

Noté par l'IA voir l'avis de non-responsabilité

Enseigné en Anglais

Découvrez comment les employés des entreprises prestigieuses maîtrisent des compétences recherchées

En savoir plus sur Coursera pour les affaires

logos de Petrobras, TATA, Danone, Capgemini, P&G et L'Oreal

Élaborez votre expertise du sujet

Ce cours fait partie de la Spécialisation "Mastering NLP: Tokenization, Sentiment Analysis & Neural MT "

Lorsque vous vous inscrivez à ce cours, vous êtes également inscrit(e) à cette Spécialisation.

Apprenez de nouveaux concepts auprès d'experts du secteur
Acquérez une compréhension de base d'un sujet ou d'un outil
Développez des compétences professionnelles avec des projets pratiques
Obtenez un certificat professionnel partageable

Il y a 5 modules dans ce cours

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.

Inclus

22 vidéos5 lectures5 devoirs1 sujet de discussion

22 vidéos 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 lectures 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 devoirs 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 sujet de discussion 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.

Inclus

21 vidéos5 lectures5 devoirs

21 vidéos 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 lectures 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 devoirs 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.

Inclus

14 vidéos4 lectures4 devoirs

14 vidéos 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 lectures 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 devoirs 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.

Inclus

6 vidéos2 lectures2 devoirs

6 vidéos 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 lectures Total 25 minutes

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

2 devoirs 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.

Inclus

1 vidéo1 lecture2 devoirs1 sujet de discussion2 laboratoires non notés

1 vidéo Total 3 minutes

Course Summary: Neural Models and Machine Translation 3 minutes

1 lecture Total 60 minutes

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

2 devoirs Total 60 minutes

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

1 sujet de discussion Total 10 minutes

Describe your Learning Journey 10 minutes

2 laboratoires non notés Total 120 minutes

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

Obtenez un certificat professionnel

Ajoutez ce titre à votre profil LinkedIn, à votre curriculum vitae ou à votre CV. Partagez-le sur les médias sociaux et dans votre évaluation des performances.

Instructeur

Edureka

160 Cours 149 681 apprenants

Offert par

Edureka

En savoir plus sur Machine Learning

IBM
Generative AI Language Modeling with Transformers
Cours
Packt
Sequence Modeling, Transformers, and Transfer Learning
Cours
Packt
Natural Language Processing - Deep Learning Models in Python
Cours
Pearson
Introduction to Transformer Models for NLP: Unit 1
Cours

Pour quelles raisons les étudiants sur Coursera nous choisissent-ils pour leur carrière ?

Felipe M.

Étudiant(e) depuis 2018

’Pouvoir suivre des cours à mon rythme à été une expérience extraordinaire. Je peux apprendre chaque fois que mon emploi du temps me le permet et en fonction de mon humeur.’

Jennifer J.

Étudiant(e) depuis 2020

’J'ai directement appliqué les concepts et les compétences que j'ai appris de mes cours à un nouveau projet passionnant au travail.’

Larry W.

Étudiant(e) depuis 2021

’Lorsque j'ai besoin de cours sur des sujets que mon université ne propose pas, Coursera est l'un des meilleurs endroits où se rendre.’

Chaitanya A.

’Apprendre, ce n'est pas seulement s'améliorer dans son travail : c'est bien plus que cela. Coursera me permet d'apprendre sans limites.’

Ouvrez de nouvelles portes avec Coursera Plus

Accès illimité à 10,000+ cours de niveau international, projets pratiques et programmes de certification prêts à l'emploi - tous inclus dans votre abonnement.

Faites progresser votre carrière avec un diplôme en ligne

Obtenez un diplôme auprès d’universités de renommée mondiale - 100 % en ligne

Découvrir les diplômes

Rejoignez plus de 3 400 entreprises mondiales qui ont choisi Coursera pour les affaires

Améliorez les compétences de vos employés pour exceller dans l’économie numérique

Foire Aux 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.

Plus de questions

Visitez le Centre d'Aide pour les Étudiants

Aide financière disponible,

¹ Certains travaux de ce cours sont notés par l'IA. Pour ces travaux, vos Données internes seront utilisées conformément à Notification de confidentialité de Coursera.

Neural Models and Machine Translation

Ce cours n'est pas disponible en Français (France)

Neural Models and Machine Translation

Ce que vous apprendrez

Compétences que vous acquerrez

Outils que vous découvrirez

Détails à connaître

Découvrez comment les employés des entreprises prestigieuses maîtrisent des compétences recherchées

Élaborez votre expertise du sujet

Il y a 5 modules dans ce cours

Neural Language Models

Inclus

Machine Translation (MT)

Inclus

Speech and Multimodal NLP

Inclus

Building Chatbots

Inclus

Course Wrap-up and Assessments

Inclus

Obtenez un certificat professionnel

Instructeur

Offert par

En savoir plus sur 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

Pour quelles raisons les étudiants sur Coursera nous choisissent-ils pour leur carrière ?

Felipe M.

Jennifer J.

Larry W.

Chaitanya A.

Ouvrez de nouvelles portes avec Coursera Plus

Faites progresser votre carrière avec un diplôme en ligne

Rejoignez plus de 3 400 entreprises mondiales qui ont choisi Coursera pour les affaires

Foire Aux Questions

What is the process of neural machine translation?

Neural Machine Translation: What Is It?

Why this Course?

Plus de questions