Neural Models and Machine Translation

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

Instructor: Edureka

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

18 hours to complete

3 weeks at 6 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

18 hours to complete

3 weeks at 6 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

Details to know

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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.

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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 prompt1 plugin

22 videosTotal 102 minutes

Specialization Introduction4 minutesPreview module
Course Introduction3 minutes
Word Embeddings and Their Role in NLP5 minutes
RNNs and LSTMs in NLP Applications5 minutes
Sequence-to-Sequence (Seq2Seq) Models3 minutes
Introduction to Transformers3 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 NLP3 minutes
Self-Attention in Transformers3 minutes
Multi-Head Attention in BERT4 minutes
Visualizing Attention Mechanisms3 minutes
Subword Tokenization (WordPiece, SentencePiece, Unigram Models)5 minutes
Byte-Pair Encoding (BPE) in Transformers5 minutes
Tokenization Pipelines in Hugging Face4 minutes
Handling Out-of-Vocabulary (OOV) Words in Pretrained Models4 minutes
Demonstration: Tokenization of Low-resource Language4 minutes
BERT: Fine-Tuning and Real-World Applications5 minutes
GPT Models: Understanding and Use Cases3 minutes
Fine-Tuning Transformers for NLP Tasks4 minutes
Transfer Learning for NLP3 minutes

5 readingsTotal 65 minutes

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

5 assignmentsTotal 54 minutes

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

1 discussion promptTotal 10 minutes

Introduce Yourself10 minutes

1 pluginTotal 15 minutes

Understanding Check: NLP and Transformer Models15 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 assignments1 plugin

21 videosTotal 112 minutes

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

5 readingsTotal 75 minutes

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

5 assignmentsTotal 54 minutes

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

1 pluginTotal 15 minutes

Understanding Check: Machine Translation15 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 assignments1 plugin

14 videosTotal 68 minutes

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

4 readingsTotal 95 minutes

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

4 assignmentsTotal 48 minutes

Knowledge Check: Speech and Multimodal NLP30 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 Efficiency6 minutes

1 pluginTotal 15 minutes

Understanding Check: Speech Recognition and Multimodal Learning15 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 assignments1 plugin

6 videosTotal 30 minutes

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

2 readingsTotal 25 minutes

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

2 assignmentsTotal 36 minutes

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

1 pluginTotal 15 minutes

Understanding Check: Chatbot Frameworks and Performance15 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 videoTotal 2 minutes

Course Summary: Neural Models and Machine Translation2 minutesPreview module

1 readingTotal 60 minutes

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

2 assignmentsTotal 60 minutes

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

1 discussion promptTotal 10 minutes

Describe your Learning Journey10 minutes

2 ungraded labsTotal 120 minutes

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

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Instructor

Edureka

71 Courses87,640 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

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

The course may not offer an audit option. 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.

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. If you only want to read and view the course content, you can audit the course for free.