Let us help you find the training program you are looking for.

If you can't find what you are looking for, contact us, we'll help you find it. We have over 800 training programs to choose from.

Natural Language Processing for Hackers

  • Course Code: Data Science - Natural Language Processing for Hackers
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 6 Days Audience: This course is geared for Python experienced developers, analysts or others who wants to know the techniques to analyze, interpret, and create human-understandable text and speech. Advances in machine learning have pushed NLP to new levels of accuracy and uncanny realism

Course Snapshot 

  • Course: Natural Language Processing for Hackers 
  • Duration: 6 days 
  • Skill-level: Foundation-level Natural-Language-Processing skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who wants to know the techniques to analyze, interpret, and create human-understandable text and speech. Advances in machine learning have pushed NLP to new levels of accuracy and uncanny realism 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Natural Language Processing for Hackers covers NLP end-to-end, giving you the skills and techniques that allow your computers to speak human. Unlike many research-oriented courses that use the kind of clean datasets you would never find in the real world; this practical guide takes on NLP as you’ll actually use it. You’ll learn the key concepts of NLP by coding your own tools and projects, from a text analysis service right up to a full-featured chatbot. Everything is written in concise, easy-to-read Python code to ensure you’ll grok the most important aspects of Natural Language Processing. When you’re done, you will be able to apply the complete range of NLP techniques to build practical applications—even with messy real-world data. 

Working in a hands-on learning environment, led by our Natural Language Processing expert instructor, students will learn about and explore: 

  • You’ll learn the key concepts of NLP by coding your own tools and projects, from a text analysis service right up to a full-featured chatbot. Everything is written in concise, easy-to-read Python code to ensure 
  • you’ll grok the most important aspects of Natural Language Processing.  
  • you will be able to apply the complete range of NLP techniques to build practical applications—even with messy real-world data. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Constructing your own Text Analysis engine 
  • Building a Twitter listener that performs Sentiment Analysis on a certain subject 
  • Assembling your own NLP toolbox, complete with Part of Speech Tagger, Shallow Parser, Named Entity Extractor, and Dependency Parsers 
  • Cleaning and standardizing messy datasets 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to who wants to know the techniques to analyze, interpret, and create human-understandable text and speech 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills.  
  • Attendees requires familiarity with Python, but no prior knowledge of natural language processing or machine learning is necessary. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

PART: INTRODUCTION TO NLTK  

  1. NLTK FUNDAMENTALS 
  • Installing NLTK  
  • Splitting Text  
  • Building a vocabulary 
  • Fun with Bigrams and Trigrams 
  • Part of Speech Tagging  
  • Named Entity Recognition 
  1. GETTING STARTED WITH WORDNET  
  • Wordnet Structure  
  • Lemma Operations  
  1. LEMMATIZING AND STEMMING  
  • How stemmers work  
  • How lemmatizes work 

PART: CREATE A TEXT ANALYSIS SERVICE  

  1. INTRODUCTION TO MACHINE LEARNING  
  • A Practical Machine Learning Example  
  1. GETTING STARTED WITH SCIKIT-LEARN  
  • Installing Scikit-Learn and building a dataset  
  • Training a Scikit-Learn Model 
  • Making Predictions  
  1. FINDING THE DATA  
  • Existing corpora  
  • Ideas for Gathering Data 
  • Getting the Data  
  1. LEARNING TO CLASSIFY TEXT 
  • Text Feature Extractor  
  • Scikit-Learn Feature Extraction  
  • Text Classification with Naive Bayes  
  1. PERSISTING MODELS  
  1. BUILDING THE API  
  • Building a Flask API Deploy to Heroku 

PART: CREATE A SOCIAL MEDIA MONITORING SERVICE  

  1. BASICS OF SENTIMENT ANALYSIS  
  • Be Aware of Negations  
  • Machine Learning doesn’t get Humor  
  • Multiple and Mixed Sentiments  
  • Non-Verbal Communication  
  1. TWITTER SENTIMENT DATA  
  • Twitter Corpora  
  • Other Sentiment Analysis Corpora  
  • Building a Tweets Dataset  
  • Sentiment Analysis – A First Attempt  
  • Better Tokenization  
  1. FINE TUNING  
  • Try a different classifier  
  • Use Ngrams Instead of Words  
  • Using a Pipeline  
  • Cross Validation  
  • Grid Search  
  • Picking the Best Tokenizer  
  1. BUILDING THE TWITTER LISTENER 
  1. CLASSIFICATION METRICS  
  • Binary Classification  
  1. MULTI-CLASS METRICS  
  • The Confusion Matrix 
  1. BUILD YOUR OWN PART-0F-SPEECH TAGGER  
  • Part-Of-Speech Corpora  
  • Building Toy Models  
  • About Feature Extraction  
  • Using the NLTK Base Classes  
  • Writing the Feature Extractor  
  • Training the Tagger  
  • Out-Of-Core Learning  
  1. BUILD A CHUNKER 
  • IOB Tagging  
  • Implementing the Chunk Parser 
  • Chunker Feature Detection 
  1. BUILD A NAMED ENTITY EXTRACTOR 
  • NER Corpora  
  • The Groningen Meaning Bank Corpus 
  • Feature Detection  
  • NER Training 

PART: BUILD YOUR OWN CHATBOT ENGINE  

  1. GENERAL ARCHITECTURE   
  • Train the Platform via Examples 
  • Action Handlers  
  1. BUILDING THE CORE  
  • Chatbot Base Class and Training Set 
  • Training the Chatbot Everything together  
  1. MOVIEBOT  
  • The Movie DB API 
  • Small-Talk Handlers  
  • Simple Handlers  
  • Execution Handlers  
  1. MOVIEBOT ON FACEBOOK  
  • Installing ngrok Setting up Facebook 

Student Materials: Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable). Our courses also include step by step hands-on lab instructions and and solutions, clearly illustrated for users to complete hands-on work in class, and to revisit to review or refresh skills at any time. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work. 

View All Courses

    Course Inquiry

    Fill in the details below and we will get back to you as quickly as we can.

    Interested in any of these related courses?