NLP Boot Camp / Hands-on Natural Language Processing Training in United States of America

• 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.

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

• 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.
• Discover how to collect data from the web and navigate through semi-structured data, opening up a wealth of data sources for your projects.
• 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.
• Learn to analyze text sentiment, train emotion detectors, and interpret the results, providing a way to gauge public opinion or understand customer feedback.

Day 1

1. Launch into the Universe of Natural Language Processing

• The journey begins: Unravel the layers of NLP
• Navigating through the history of NLP
• Merging paths: Text Analytics and NLP
• Decoding language: Word Sense Disambiguation and Sentence Boundary Detection
• First steps towards an NLP Project

2. Unleashing the Power of Feature Extraction

• Dive into the vast ocean of Data Types
• Purification process: Cleaning Text Data
• Excavating knowledge: Extracting features from Texts
• Drawing connections: Finding Text Similarity through Feature Extraction

Day 2

3. Engineer Your Text Classifier

• The new era of Machine Learning and Supervised Learning
• Architecting a Text Classifier
• Constructing efficient workflows: Building Pipelines for NLP Projects
• Ensuring continuity: Saving and Loading Models

4. Master the Art of Web Scraping and API Usage

• Stepping into the digital world: Introduction to Web Scraping and APIs
• The great heist: Collecting Data by Scraping Web Pages
• Navigating through the maze of Semi-Structured Data

Day 3

5. Unearth Hidden Themes with Topic Modeling

• Embark on the path of Topic Discovery
• Decoding algorithms: Understanding Topic-Modeling Algorithms
• Dialing the right numbers: Key Input Parameters for LSA Topic Modeling
• Tackling complexity with Hierarchical Dirichlet Process (HDP)

6. Delving Deep into Vector Representations

• The Geometry of Language: Introduction to Vectors in NLP

7. Text Manipulation: Generation and Summarization

• Playing the creator: Generating Text with Markov Chains
• Distilling knowledge: Understanding Text Summarization and Key Input Parameters for TextRank
• Peering into the future: Recent Developments in Text Generation and Summarization
• Solving real-world problems: Addressing Challenges in Extractive Summarization

8. Riding the Wave of Sentiment Analysis

• Unveiling emotions: Introduction to Sentiment Analysis Tools
• Demystifying the Textblob library
• Preparing the canvas: Understanding Data for Sentiment Analysis
• Training your own emotion detectors: Building Sentiment Models

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. (1 hour)
• Tools Used: Python, Various Python Libraries (based on project needs)

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.

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.