NLP Boot Camp / Hands-On Natural Language Processing (TTAI3030)

At Course Completion

This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you’ll learn how to:

· Master the fundamentals of Natural Language Processing (NLP) and understand how it can help in making sense of text data for valuable insights.

· Develop the ability to transform raw text into a structured format that machines can understand and analyze.

· Learn how to implement sentiment analysis and topic modeling to extract meaning from text data and identify trends.

· Gain proficiency in applying machine learning and deep learning techniques to text data for tasks such as classification and prediction.

· Acquire the skills to design and build simple chatbots and question-answering systems to automate text-based interactions.

· Bonus / Optional: Leverage innovative GPT and Generative AI technologies with NLP

If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goals.

Audience Profile

This in an intermediate and beyond-level course is geared for experienced Python developers looking to delve into the exciting field of Natural Language Processing. It is ideally suited for roles such as data analysts, data scientists, machine learning engineers, or anyone working with text data and seeking to extract valuable insights from it. If you're in a role where you're tasked with analyzing customer sentiment, building chatbots, or dealing with large volumes of text data, this course will provide you with practical, hands-on skills that you can apply right away.

Prerequisites

· Proficiency in Python: As the course involves Python for hands-on labs and examples, attendees should have a good understanding of Python programming, including data structures, control flow, and basic coding practices.

· Basic knowledge of Machine Learning: Understanding the principles of machine learning, including concepts like training and testing splits, model evaluation, and overfitting, will be beneficial.

· Familiarity with Linear Algebra and Statistics: Some fundamental concepts in linear algebra (such as vectors and matrices) and statistics (mean, median, standard deviation, etc.) are essential for understanding the theory behind NLP.

· Experience with any Data Analysis Libraries: Having experience with Python data analysis libraries like Pandas, NumPy, or Matplotlib can be beneficial as they are often used in the preprocessing and analysis of text data.

· General Understanding of Natural Language Processing: While not strictly necessary, having a basic understanding of what NLP is and its potential applications can help attendees contextualize the learnings better.

Take Before: Students should have incoming practical skills aligned with those in the course(s) below, or should have attended the following course(s) as a pre-requisite:

· TTPS4876 Intermediate Python Programming for Data Science

· TTML5503 Introduction to AI, AI Programming and Machine Learning

Outline

Day 1

1. Introduction to Natural Language Processing (NLP)

· Understand what NLP is and its importance.

· Differences between natural languages and programming languages.

· Practical applications of NLP.

· How computers interpret language.

· The main concepts and techniques in NLP.

· LP pipeline introduction.

· Python's NLP libraries

· Lab: Setting up the environment and exploring basic text processing in Python.

2. Text Processing and Analysis (2 Hours)

· Tokenization, lemmatization, and stemming.

· Part-of-speech tagging role in NLP.

· Text data cleaning techniques.

· Concept of stop words and their removal.

· Regular expressions for advanced text processing tasks.

· Named Entity Recognition (NER) introduction.

· Lab: Text preprocessing on a sample text dataset.

3. Text Representation and Feature Extraction

· Bag-of-Words and TF-IDF for text representation.

· Word embeddings and their importance.

· Creating word embeddings using Word2Vec and GloVe.

· Limitations of these techniques and contextual embeddings introduction.

· Advanced methods like BERT.

· Feature extraction techniques for NLP tasks.

· Lab: Creating word embeddings using Word2Vec on a sample dataset.

Day 2:

4. Sentiment Analysis

· Sentiment analysis concept and applications.

· Techniques for sentiment analysis.

· Training a model for sentiment analysis.

· Handling negations and multi-word expressions in sentiment analysis.

· Emotion detection and opinion mining.

· Evaluating a sentiment analysis model.

· Lab: Developing a sentiment analysis model on a movie reviews dataset.

5. Topic Modeling

· Topic modeling concept and applications.

· Techniques like Latent Dirichlet Allocation (LDA) for topic modeling.

· Non-negative Matrix Factorization (NMF) for topic modeling.

· Interpreting topic modeling results.

· Tuning topic models.

· Coherence score for determining the optimal number of topics.

· Lab: Developing a topic modeling system on a news dataset.

Day 3

6. Text Classification

· Text classification concept and applications.

· Feature selection and dimensionality reduction in text classification.

· Training a text classification model.

· Handling imbalanced classes in text classification.

· Evaluation of text classification models.

· Multi-label text classification.

· Lab: Developing a text classification system on a customer complaints dataset.

7. Sequence-to-sequence Models and Chatbots

· Basics of sequence-to-sequence models.

· Encoder-Decoder architecture.

· Concept of attention in sequence-to-sequence models.

· Applications of sequence-to-sequence models in machine translation and chatbot development. • Design and implementation of chatbots.

· Limitations and challenges in developing chatbots.

· Lab: Developing a simple chatbot.

Optional / Bonus Content: Follow On

Optional: Capstone Project

· Apply the skills learned throughout the course.

· Define the problem and gather the data.

· Conduct exploratory data analysis for text data.

· Carry out preprocessing and feature extraction.

· Select and train a model.

· Evaluate the model and interpret the results.

· Lab: Complete a capstone project where students tackle a real-world NLP problem from start to finish.

Bonus Chapter: Generative AI and NLP

· Introduction to Generative AI and its role in NLP.

· Overview of Generative Pretrained Transformer (GPT) models. • Using GPT models for text generation and completion.

· Applying GPT models for improving autocomplete features.

· Use cases of GPT in question answering systems and chatbots.

· Lab: Implementing a text completion application using GPT-3.

Bonus Chapter: Advanced Applications of NLP with GPT

· Fine-tuning GPT models for specific NLP tasks.

· Using GPT for sentiment analysis and text classification.

· Role of GPT in Named Entity Recognition (NER).

· Application of GPT in developing advanced chatbots.

· Ethics and limitations of GPT and generative AI technologies.

· Lab: Fine-tuning GPT-3 for a specific NLP task.