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

本课程是 Mastering NLP: Tokenization, Sentiment Analysis & Neural MT 专项课程的一部分

位教师：Edureka

包含在 Coursera Plus 中

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5个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

5个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

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.

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积累特定领域的专业知识

本课程是 Mastering NLP: Tokenization, Sentiment Analysis & Neural MT 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

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该课程共有5个模块

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.

涵盖的内容

22个视频5篇阅读材料5个作业1个讨论话题1个插件

22个视频总计102分钟

Specialization Introduction4分钟
Course Introduction3分钟
Word Embeddings and Their Role in NLP5分钟
RNNs and LSTMs in NLP Applications5分钟
Sequence-to-Sequence (Seq2Seq) Models3分钟
Introduction to Transformers3分钟
Pretraining & Fine-Tuning Paradigm (GPT-4, T5x, BART)5分钟
Demonstration: Intelligent Query Response System for Customer Support (Word Embedding and Query Classification)6分钟
Demonstration: Intelligent Query Response System for Customer Support (Seq2Seq Model and Fine Tuning T5 Model)7分钟
Why Attention is Important in NLP3分钟
Self-Attention in Transformers3分钟
Multi-Head Attention in BERT4分钟
Visualizing Attention Mechanisms3分钟
Subword Tokenization (WordPiece, SentencePiece, Unigram Models)5分钟
Byte-Pair Encoding (BPE) in Transformers5分钟
Tokenization Pipelines in Hugging Face4分钟
Handling Out-of-Vocabulary (OOV) Words in Pretrained Models4分钟
Demonstration: Tokenization of Low-resource Language4分钟
BERT: Fine-Tuning and Real-World Applications5分钟
GPT Models: Understanding and Use Cases3分钟
Fine-Tuning Transformers for NLP Tasks4分钟
Transfer Learning for NLP3分钟

5篇阅读材料总计65分钟

Neural Architectures and Translation Challenges in Modern NLP15分钟
Attention Mechanisms in NLP: Understanding What Matters15分钟
Tokenization Techniques for Pretrained Language Models15分钟
Transformers and Pretrained Models in NLP15分钟
Module Summary: Neural Language Models5分钟

5个作业总计54分钟

Practice Quiz: Neural Networks for NLP6分钟
Practice Quiz: Attention Mechanisms in NLP6分钟
Practice Quiz: Tokenization for Pretrained Models6分钟
Practice Quiz: Transformers and Pretrained Models6分钟
Knowledge Check: Neural Language Models30分钟

1个讨论话题总计10分钟

Introduce Yourself10分钟

1个插件总计15分钟

Understanding Check: NLP and Transformer Models15分钟

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.

涵盖的内容

21个视频5篇阅读材料5个作业1个插件

21个视频总计112分钟

Evolution of Machine Translation: Rule-Based to Neural MT5分钟
Challenges in Machine Translation (Data Sparsity, Morphological Complexity)6分钟
Evaluation Metrics (BLEU Score, ROUGE, METEOR)4分钟
Unsupervised Machine Translation5分钟
Multilingual Pretraining Strategies for Zero-Shot Translation5分钟
What is Neural Machine Translation (NMT)?5分钟
Encoder-Decoder Architecture in NMT4分钟
Using LSTMs for Sequence Modeling in Translation5分钟
Attention Mechanisms in NMT5分钟
Training an NMT Model4分钟
Demonstration: Using LSTMS for Sequence Modeling in Translation6分钟
How Transformers Revolutionized MT (BERT, MarianMT)5分钟
Multilingual NLP and Zero-Shot Translation4分钟
Low-Resource Language Translation Techniques4分钟
Zero-Shot Translation Techniques3分钟
Demonstration: Zero-Shot Translation for Low Resource languages6分钟
Types of Errors (Lexical, Syntactic, Semantic, Pragmatic)3分钟
Detecting and Fixing Common Translation Errors6分钟
Customizing Translation Pipelines for Domain-Specific Needs5分钟
Reinforcement Learning from Human Feedback (RLHF) for Translation Fine-Tuning6分钟
Demonstration: Error detection in English to French Translation5分钟

5篇阅读材料总计75分钟

Introduction to Machine Translation20分钟
Neural Machine Translation (NMT)15分钟
Transformer-Based Machine Translation15分钟
Error Handling and Custom Translation Pipelines15分钟
Module Summary: Machine Translation (MT)10分钟

5个作业总计54分钟

Practice Quiz: Introduction to Machine Translation6分钟
Practice Quiz: Neural Machine Translation (NMT)6分钟
Practice Quiz: Transformer-Based Machine Translation6分钟
Practice Quiz: Error Handling and Custom Translation Pipelines6分钟
Knowledge Check: Machine Translation (MT)30分钟

1个插件总计15分钟

Understanding Check: Machine Translation15分钟

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.

涵盖的内容

14个视频4篇阅读材料4个作业1个插件

14个视频总计68分钟

Fundamentals of Speech Recognition4分钟
Automatic Speech Recognition (ASR) Models (e.g., Whisper, DeepSpeech)5分钟
Challenges in Speech Processing4分钟
TTS Applications in NLP3分钟
Demonstration: Building a Voice Narration Assistant4分钟
Introduction to Multimodal Learning4分钟
Vision-Language Models (CLIP, BLIP)5分钟
Integrating Text and Speech in NLP Applications4分钟
Generative Models for Multimodal Content5分钟
Model Compression (Quantization, Pruning, Knowledge Distillation)5分钟
Real-Time NLP for Edge Devices4分钟
Explainability in Deep NLP Models4分钟
Ethical AI in NLP5分钟
Emerging Trends (GPT-5, HyperNetworks, Autonomous AI Agents)6分钟

4篇阅读材料总计95分钟

Understanding Speech-to-Text and Text-to-Speech: Foundations of Speech Processing in NLP30分钟
Exploring Multimodal NLP: Integrating Text, Vision, and Speech for Richer AI Understanding20分钟
Future Directions in NLP: Model Efficiency, Ethics, and Emerging Trends15分钟
Module Summary: Speech and Multimodal NLP30分钟

4个作业总计48分钟

Practice Quiz: Speech-to-Text and Text-to-Speech (TTS)6分钟
Practice Quiz: Multimodal NLP (Text + Images + Audio)6分钟
Practice Quiz: Future Trends in NLP and Model Efficiency6分钟
Knowledge Check: Speech and Multimodal NLP30分钟

1个插件总计15分钟

Understanding Check: Speech Recognition and Multimodal Learning15分钟

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.

涵盖的内容

6个视频2篇阅读材料2个作业1个插件

6个视频总计30分钟

NLP for Chatbot Frameworks5分钟
Understanding Intents and Entities in Chatbots3分钟
Chatbot Integration with Business Workflows4分钟
Fine-Tuning Models for Contextual Chatbots6分钟
Evaluating Chatbot Performance (Accuracy, Coherence, User Feedback)3分钟
Demonstration: Building Multiturn Chatbots7分钟

2篇阅读材料总计25分钟

Understanding Chatbots: NLP, Intents, Entities,Integration, and Contextual Fine-Tuning15分钟
Module Summary: Building Chatbots10分钟

2个作业总计36分钟

Practice Quiz: Building Chatbots6分钟
Knowledge Check: Building Chatbots30分钟

1个插件总计15分钟

Understanding Check: Chatbot Frameworks and Performance15分钟

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.

涵盖的内容

1个视频1篇阅读材料2个作业1个讨论话题2个非评分实验室

1个视频总计2分钟

Course Summary: Neural Models and Machine Translation2分钟

1篇阅读材料总计60分钟

Final Practice Project: Smart Multilingual Assistant for Global E-Commerce60分钟

2个作业总计60分钟

End Course Knowledge Check: Neural Models and Machine Translation30分钟
Designing a Neural Multilingual Assistant30分钟

1个讨论话题总计10分钟

Describe your Learning Journey10分钟

2个非评分实验室总计120分钟

Attention in NLP60分钟
Understanding NMT with Attention and BLEU Evaluation60分钟

获得职业证书

将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

位教师

Edureka

98 门课程102,796 名学生

提供方

Edureka

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常见问题

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.