Natural Language Processing (NLP) Outlines
Course ID: 2512150101307ESH
Course Dates : 15/12/25 Course Duration : 5 Studying Day/s Course Location: London, UK
Language: Bilingual
Course Category: Professional and CPD Training Programs
Course Subcategories:
Leadership and Management
Chatbot and Virtual Assistant Development
Ethical AI and Bias Mitigation
Language Modeling
Multilingual NLP and Translation Systems
Sentiment Analysis
Text Classification
Transformer Architectures
Course Information
Introduction
Natural Language Processing (NLP) is at the forefront of modern technology, bridging the gap between human communication and machines. This 5-day intensive workshop provides participants with a comprehensive understanding of NLP techniques, tools, and applications, equipping them to design and implement cutting-edge solutions in various domains. From sentiment analysis to language generation, this course covers the essentials needed to unlock the potential of NLP in professional and research environments.
Objectives
By the end of this course, participants will be able to:
Understand the foundational concepts of NLP and its significance in modern industries.
Utilize key NLP tools and libraries for data preprocessing and language modeling.
Apply techniques such as tokenization, stemming, and lemmatization to real-world problems.
Build and evaluate models for text classification, sentiment analysis, and language translation.
Implement advanced NLP techniques, including transformers and large language models.
Explore ethical considerations and future trends in NLP applications.
Who Should Attend?
This course is ideal for:
Data scientists and machine learning engineers seeking to enhance their NLP expertise.
Software developers and IT professionals interested in language-based solutions.
Academicians and researchers exploring NLP for academic projects.
Business professionals aiming to leverage NLP for decision-making and automation.
Anyone with a basic understanding of programming and an interest in language technology.
Training Method
• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a 7” Tablet containing a copy of the presentation, slides and handouts
• Post-assessment
Program Support
This program is supported by:
* Interactive discussions
* Role-play
* Case studies and highlight the techniques available to the participants.
Daily Agenda
Daily Schedule (Monday to Friday)
- 09:00 AM – 10:30 AM Technical Session 1
- 10:30 AM – 12:00 PM Technical Session 2
- 12:00 PM – 01:00 PM Technical Session 3
- 01:00 PM – 02:00 PM Lunch Break (If Applicable)
- Participants are expected to engage in guided self-study, reading, or personal reflection on the day’s content. This contributes toward the CPD accreditation and deepens conceptual understanding.
- 02:00 PM – 04:00 PM Self-Study & Reflection
Please Note:
- All training sessions are conducted from Monday to Friday, following the standard working week observed in the United Kingdom and European Union. Saturday and Sunday are official weekends and are not counted as part of the course duration.
- Coffee and refreshments are available on a floating basis throughout the morning. Participants may help themselves at their convenience to ensure an uninterrupted learning experience Provided if applicable and subject to course delivery arrangements.
- Lunch Provided if applicable and subject to course delivery arrangements.
Course Outlines
Week 1
Day 1: Introduction to NLP and Text Preprocessing
Overview of Natural Language Processing and its real-world applications.
Understanding text data: tokens, vocabulary, and corpora.
Text preprocessing: tokenization, stemming, lemmatization, and stopword removal.
Hands-on session: Cleaning and preparing textual datasets using Python and libraries like NLTK and SpaCy.
Day 2: Core NLP Techniques
Word embeddings: Word2Vec, GloVe, and FastText.
Feature extraction methods: TF-IDF and Bag of Words (BoW).
Text classification techniques: Naïve Bayes and Support Vector Machines (SVM).
Practical exercise: Building a spam detection model.
Day 3: Advanced NLP Techniques
Introduction to Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM).
Sentiment analysis using deep learning models.
Sequence-to-sequence models for language translation.
Project implementation: Building an LSTM-based sentiment analysis model.
Day 4: Transformers and Modern NLP Architectures
Understanding transformers and attention mechanisms.
Exploring BERT, GPT, and other pre-trained models.
Fine-tuning transformers for specific tasks.
Hands-on activity: Fine-tuning BERT for text classification.
Day 5: NLP Applications, Trends, and Ethics
Applications of NLP in chatbots, summarization, and language generation.
Ethical considerations in NLP: Bias, fairness, and responsible AI use.
Future trends in NLP and large language models.
Capstone project: Building an end-to-end NLP solution.
Overview of Natural Language Processing and its real-world applications.
Understanding text data: tokens, vocabulary, and corpora.
Text preprocessing: tokenization, stemming, lemmatization, and stopword removal.
Hands-on session: Cleaning and preparing textual datasets using Python and libraries like NLTK and SpaCy.
Day 2: Core NLP Techniques
Word embeddings: Word2Vec, GloVe, and FastText.
Feature extraction methods: TF-IDF and Bag of Words (BoW).
Text classification techniques: Naïve Bayes and Support Vector Machines (SVM).
Practical exercise: Building a spam detection model.
Day 3: Advanced NLP Techniques
Introduction to Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM).
Sentiment analysis using deep learning models.
Sequence-to-sequence models for language translation.
Project implementation: Building an LSTM-based sentiment analysis model.
Day 4: Transformers and Modern NLP Architectures
Understanding transformers and attention mechanisms.
Exploring BERT, GPT, and other pre-trained models.
Fine-tuning transformers for specific tasks.
Hands-on activity: Fine-tuning BERT for text classification.
Day 5: NLP Applications, Trends, and Ethics
Applications of NLP in chatbots, summarization, and language generation.
Ethical considerations in NLP: Bias, fairness, and responsible AI use.
Future trends in NLP and large language models.
Capstone project: Building an end-to-end NLP solution.
