The Hands-on Natural Language Processing (NLP) Boot Camp is an immersive, three-day course that serves as your guide to building machines that can read and interpret human language. NLP is a unique interdisciplinary field, blending computational linguistics with artificial intelligence to help machines understand, interpret, and generate human language. In an increasingly data-driven world, NLP skills provide a competitive edge, enabling the development of sophisticated projects such as voice assistants, text analyzers, chatbots, and so much more.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

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.

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

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

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

9

  • 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.

10

  • 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.

11

  • 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.

Audience

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.

Language

English

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: TTPS4873 Fast Track to Python for Data Science TTML5503 Introduction to AI, AI Programming and Machine Learning

$2,395

Length: 3.0 days (24 hours)

Level:

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04
Dec
Monday
10:00 AM ET -
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