Natural Language Processing (NLP) with Deep Learning Training Course

Introduction

Natural Language Processing (NLP), powered by Deep Learning (DL), is at the forefront of enabling machines to understand, interpret, and generate human language with unprecedented accuracy. This Natural Language Processing (NLP) with Deep Learning Training Course is an intensive program designed for AI/ML engineers, data scientists, software developers, and linguists eager to master the advanced techniques required to process and derive insights from textual data. Participants will explore how Artificial Intelligence is transforming applications like sentiment analysis, machine translation, text summarization, and conversational AI, moving beyond traditional NLP methods.

The curriculum provides a solid foundation in text preprocessing and feature engineering, quickly advancing to the powerful realm of deep learning for NLP. You will gain hands-on experience with cutting-edge architectures such as Recurrent Neural Networks (RNNs), LSTMs, GRUs, and the revolutionary Transformer models. A significant focus will be placed on understanding and leveraging Large Language Models (LLMs) like BERT and GPT, including techniques for fine-tuning them for specific tasks. By the end of this course, you will be equipped to build sophisticated language understanding and generation systems, ready to tackle complex challenges in the domain of text analysis and AI-driven language applications.

Target Audience

• AI/ML Engineers and Data Scientists.
• Software Developers specializing in text-based applications.
• Computational Linguists and NLP Researchers.
• Data Analysts dealing with large volumes of unstructured text.
• Professionals interested in building chatbots or intelligent assistants.

Duration

10 days

Course Objectives

1. Understand the fundamental concepts of Natural Language Processing and its applications.
2. Master text preprocessing techniques and traditional feature engineering for textual data.
3. Apply traditional Machine Learning algorithms for basic NLP tasks like text classification.
4. Grasp the concept and utility of various word embeddings (Word2Vec, GloVe, FastText).
5. Implement Recurrent Neural Networks (RNNs), LSTMs, and GRUs for sequence modeling in NLP.
6. Understand and apply the Transformer architecture and attention mechanism for advanced NLP tasks.
7. Learn to work with and fine-tune Large Language Models (LLMs) like BERT and GPT.
8. Explore practical applications such as text generation, summarization, machine translation, and conversational AI.

Course Content

Module 1. Foundations of Natural Language Processing

• What is NLP: Definition, goals, and history
• Overview of core NLP tasks: Tokenization, POS tagging, NER, parsing
• Challenges in NLP: Ambiguity, context, linguistic variations
• Applications of NLP across various domains: Healthcare, finance, customer service
• Introduction to common NLP libraries: NLTK, SpaCy, Hugging Face Transformers

Module 2. Text Preprocessing and Feature Engineering

• Text cleaning: Lowercasing, punctuation removal, special character handling
• Tokenization: Word, sentence, and subword tokenization
• Normalization: Stemming and Lemmatization
• Stop word removal and handling rare words
• Traditional text representation: Bag-of-Words (BoW), TF-IDF, N-grams

Module 3. Traditional Machine Learning for NLP

• Text classification using classic ML algorithms: Naive Bayes, Logistic Regression, SVM
• Sentiment analysis basics: Rule-based vs. ML-based approaches
• Introduction to Named Entity Recognition (NER) with traditional methods
• Evaluation metrics for NLP tasks: Accuracy, Precision, Recall, F1-score
• Feature selection for traditional text classifiers

Module 4. Word Embeddings

• Limitations of one-hot encoding and traditional text representations
• The concept of distributed representations and dense vectors
• Word2Vec: Skip-gram and CBOW models
• GloVe (Global Vectors for Word Representation)
• FastText: Handling out-of-vocabulary words and subword information
• Visualizing word embeddings and exploring semantic relationships

Module 5. Recurrent Neural Networks (RNNs) for NLP

• Introduction to Neural Networks for sequential data
• Basic RNN architecture and its challenges (vanishing/exploding gradients)
• Long Short-Term Memory (LSTM) networks: Architecture and gate mechanisms
• Gated Recurrent Units (GRUs): Simplified alternative to LSTMs
• Implementing RNNs, LSTMs, and GRUs in TensorFlow/PyTorch for text classification

Module 6. Advanced RNN Architectures

• Sequence-to-Sequence (Seq2Seq) models for mapping input sequences to output sequences
• Encoder-Decoder architecture for complex NLP tasks
• Bidirectional RNNs for capturing context from both directions
• Applications of Seq2Seq: Machine translation, chatbots, text summarization
• Training strategies for sequence models: Teacher forcing

Module 7. Transformer Models & Attention Mechanism

• The limitation of RNNs with long sequences
• Introduction to the Attention mechanism: Focusing on relevant parts of the input
• Self-Attention mechanism: Relating different positions of a single sequence
• The Transformer architecture: Encoder and Decoder blocks, Multi-Head Attention
• Positional Encoding: Preserving sequence order information

Module 8. Large Language Models (LLMs) & Fine-tuning

• Introduction to pre-trained LLMs: BERT, GPT, RoBERTa, T5
• Transfer learning in NLP: Pre-training and fine-tuning paradigm
• Understanding masked language modeling and next-sentence prediction (BERT)
• Autoregressive pre-training (GPT) for text generation
• Fine-tuning LLMs for specific downstream tasks (e.g., classification, Q&A)

Module 9. Text Generation and Summarization

• Generative models for text: From RNN-based to Transformer-based generation
• Sampling strategies: Greedy search, Beam search, Nucleus sampling
• Extractive summarization: Identifying key sentences/phrases
• Abstractive summarization: Generating new summary sentences
• Evaluating text generation and summarization quality (BLEU, ROUGE scores)

Module 10. Machine Translation

• Introduction to Neural Machine Translation (NMT)
• Seq2Seq models with attention for NMT
• Challenges in machine translation: Word alignment, idiomatic expressions
• Using pre-trained NMT models and APIs
• Evaluation metrics for machine translation: BLEU score

Module 11. Conversational AI & Chatbots

• Components of a conversational AI system: NLU, Dialogue Management, NLG
• Intent recognition and Entity extraction using Deep Learning
• Dialogue state tracking and response generation
• Building rule-based vs. AI-powered chatbots
• Introduction to conversational AI platforms and frameworks

Module 12. Ethical NLP & Future Trends

• Bias in NLP models: Gender bias, racial bias, stereotypes
• Fairness and transparency in language technologies
• Privacy concerns related to text data and language models
• Explainable NLP (XNLP): Interpreting model decisions
• Emerging trends: Multimodal NLP, low-resource NLP, ethical AI guidelines for language models

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank