"Hello" → "Γεια σας" → "Hola" → "Bonjour" → "こんにちは" → "مرحبا"

Natural Language Processing

Create intelligent language models and conversational AI systems using transformers, BERT, and GPT architectures

10 Weeks • Advanced Level
NLP Specialists
Conversational AI

Course Overview

Master the art and science of Natural Language Processing with cutting-edge techniques that power modern AI applications. From sentiment analysis to large language models, learn to build systems that understand and generate human language.

This comprehensive program covers everything from traditional NLP techniques to state-of-the-art transformer architectures like BERT, GPT, and T5. Build real-world applications including chatbots, translation systems, and content generation tools.

Transformer Mastery

Deep dive into attention mechanisms and transformer architectures powering modern NLP

Conversational AI

Build sophisticated chatbots with context understanding and personality

Multilingual NLP

Work with multiple languages and cross-lingual transfer learning techniques

€2,199
Complete Course Package
Duration 10 Weeks
Format Online + Live Sessions
Level Advanced
Projects 4 Industry Projects
Support 24/7 Mentorship
💳 Interest-free payment plans
🔄 30-day money-back guarantee

Course Curriculum

A comprehensive 10-week journey through modern natural language processing

01

NLP Fundamentals & Text Processing

Master the foundations of text processing, tokenization, and traditional NLP techniques before diving into modern approaches.

  • • Text preprocessing and normalization
  • • Tokenization and stemming
  • • N-grams and language models
  • • Part-of-speech tagging
  • • Named entity recognition
  • • Regular expressions for text mining
Week 1
02

Vector Representations & Embeddings

Learn how to represent words and documents as vectors, from traditional methods to modern embedding techniques.

  • • TF-IDF and bag-of-words
  • • Word2Vec and GloVe embeddings
  • • FastText and subword embeddings
  • • Document embeddings
  • • Dimensionality reduction techniques
  • • Similarity metrics and clustering
Week 2
03

Deep Learning for NLP

Apply neural networks to NLP tasks using RNNs, LSTMs, and CNNs for text classification and sequence modeling.

  • • RNNs for sequence modeling
  • • LSTM and GRU networks
  • • Bidirectional architectures
  • • CNNs for text classification
  • • Attention mechanisms
  • • Sequence-to-sequence models
Week 3-4
04

Transformer Architecture

Deep dive into the transformer architecture that revolutionized NLP, including self-attention and positional encoding.

  • • Self-attention mechanisms
  • • Multi-head attention
  • • Positional encoding
  • • Encoder-decoder architecture
  • • Layer normalization
  • • Transformer optimization
Week 5-6
05

Pre-trained Language Models

Master BERT, GPT, and other pre-trained models for transfer learning and fine-tuning on specific NLP tasks.

  • • BERT and its variants
  • • GPT series models
  • • T5 and unified text-to-text
  • • Fine-tuning strategies
  • • Transfer learning techniques
  • • Model compression and distillation
Week 7-8
06

Advanced Applications & Deployment

Build production-ready NLP systems including chatbots, question answering, and multilingual applications.

  • • Conversational AI systems
  • • Question answering models
  • • Text summarization
  • • Multilingual NLP
  • • Model deployment strategies
  • • Performance optimization
Week 9-10

Capstone Projects

Build cutting-edge NLP applications that demonstrate your expertise to potential employers

Intelligent Chatbot

Build a sophisticated conversational AI system with context understanding, personality, and multi-turn dialogue capabilities using transformer models.

Transformer-based dialogue system
Context-aware responses
Web interface deployment
PyTorch • Transformers • Flask • Dialogflow

News Sentiment Analytics

Create a real-time news sentiment analysis platform that processes multiple languages and provides market intelligence insights.

Multi-language sentiment analysis
Real-time data processing
Interactive dashboard
BERT • TensorFlow • FastAPI • React

Document Q&A System

Build an intelligent document question-answering system that can understand and answer questions about uploaded documents using BERT and extractive QA.

BERT-based question answering
Document parsing and indexing
Confidence scoring system
Transformers • PyTorch • Elasticsearch • Streamlit

Code Documentation Generator

Create an AI system that automatically generates comprehensive documentation for code repositories using advanced language models and code understanding.

Code-to-text generation
Multiple programming languages
GitHub integration
GPT • CodeBERT • GitHub API • Python

Prerequisites & Requirements

Technical Prerequisites

  • Python Proficiency: Strong knowledge of Python with NumPy, Pandas, and Matplotlib
  • Machine Learning Fundamentals: Understanding of supervised learning and neural networks
  • Linear Algebra: Vector operations, matrix multiplication, and eigenvalues
  • Text Processing: Basic regex and string manipulation experience

Recommended Background

  • Deep Learning: Familiarity with TensorFlow or PyTorch frameworks
  • Statistics: Understanding of probability, distributions, and hypothesis testing
  • Linguistics: Basic understanding of language structure and grammar (helpful but not required)
  • Time Commitment: 12-15 hours per week including lectures, assignments, and projects

Advanced Natural Language Processing Training

Natural language processing represents one of the most rapidly advancing fields in artificial intelligence, with applications spanning from conversational AI to content generation and language translation. Our comprehensive training program provides students with deep expertise in both traditional NLP techniques and cutting-edge transformer architectures that power modern language models.

Industry partnerships with leading technology companies ensure our curriculum covers the latest developments in language model research and practical implementation strategies. Students gain hands-on experience with the same tools and methodologies used by NLP teams at major tech companies developing production language systems.

Project-based learning methodology emphasizes building real-world NLP applications that demonstrate professional-level competency in language understanding and generation tasks. Our capstone projects challenge students to create complete systems from data preprocessing through model deployment and user interface development.

Career development support includes specialized preparation for NLP engineering roles, portfolio optimization, and networking opportunities with industry professionals working on conversational AI, content generation, and language understanding systems. This targeted approach has resulted in exceptional placement rates in specialized NLP positions.

Ready to Master NLP?

Join the next generation of NLP engineers building the future of human-computer interaction.

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💳 Flexible payment options • 🔄 30-day money-back guarantee • 🎓 Industry-recognized certification