Neural Models and Machine Translation

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Neural Models and Machine Translation

Dieser Kurs ist Teil von Spezialisierung Mastering NLP: Tokenization, Sentiment Analysis & Neural MT

Dozent: Edureka

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5 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

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Empfohlene Erfahrung

2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

5 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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.

Kompetenzen, die Sie erwerben

Kategorie: Tensorflow
Kategorie: PyTorch (Machine Learning Library)
Kategorie: Machine Learning Methods
Kategorie: Artificial Neural Networks
Kategorie: Artificial Intelligence
Kategorie: Applied Machine Learning
Kategorie: Natural Language Processing
Kategorie: Artificial Intelligence and Machine Learning (AI/ML)
Kategorie: Deep Learning
Kategorie: Machine Learning
Kategorie: Large Language Modeling

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Juli 2025

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18 Aufgaben

Unterrichtet in Englisch

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Dieser Kurs ist Teil der Spezialisierung Spezialisierung Mastering NLP: Tokenization, Sentiment Analysis & Neural MT

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In diesem Kurs gibt es 5 Module

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.

Das ist alles enthalten

22 Videos5 Lektüren5 Aufgaben1 Diskussionsthema1 Plug-in

22 VideosInsgesamt 102 Minuten

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

5 LektürenInsgesamt 65 Minuten

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

5 AufgabenInsgesamt 54 Minuten

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

1 DiskussionsthemaInsgesamt 10 Minuten

Introduce Yourself10 Minuten

1 Plug-inInsgesamt 15 Minuten

Understanding Check: NLP and Transformer Models15 Minuten

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.

Das ist alles enthalten

21 Videos5 Lektüren5 Aufgaben1 Plug-in

21 VideosInsgesamt 112 Minuten

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

5 LektürenInsgesamt 75 Minuten

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

5 AufgabenInsgesamt 54 Minuten

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

1 Plug-inInsgesamt 15 Minuten

Understanding Check: Machine Translation15 Minuten

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.

Das ist alles enthalten

14 Videos4 Lektüren4 Aufgaben1 Plug-in

14 VideosInsgesamt 68 Minuten

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

4 LektürenInsgesamt 95 Minuten

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

4 AufgabenInsgesamt 48 Minuten

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

1 Plug-inInsgesamt 15 Minuten

Understanding Check: Speech Recognition and Multimodal Learning15 Minuten

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.

Das ist alles enthalten

6 Videos2 Lektüren2 Aufgaben1 Plug-in

6 VideosInsgesamt 30 Minuten

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

2 LektürenInsgesamt 25 Minuten

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

2 AufgabenInsgesamt 36 Minuten

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

1 Plug-inInsgesamt 15 Minuten

Understanding Check: Chatbot Frameworks and Performance15 Minuten

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.

Das ist alles enthalten

1 Video1 Lektüre2 Aufgaben1 Diskussionsthema2 Unbewertete Labore

1 VideoInsgesamt 2 Minuten

Course Summary: Neural Models and Machine Translation2 MinutenModulvorschau

1 LektüreInsgesamt 60 Minuten

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

2 AufgabenInsgesamt 60 Minuten

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

1 DiskussionsthemaInsgesamt 10 Minuten

Describe your Learning Journey10 Minuten

2 Unbewertete LaboreInsgesamt 120 Minuten

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

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Dozent

Edureka

73 Kurse87.845 Lernende

von

Edureka

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Häufig gestellte Fragen

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.