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

  • Course Code: Data Science - Natural Language Processing
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for attendees with Python skills who wants to know applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries—all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before.

Course Snapshot 

  • Course: Natural Language Processing  
  • Duration: 3 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 create machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI. 
  • 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 is your guide to building machines that can read and interpret human language. In it, you’ll use readily available Python packages to capture the meaning in text and react accordingly. The course expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions. 

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 applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries—all at a low cost.  
  • New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before. 

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

  • Some sentences in this course were written by NLP! Can you guess which ones? 
  • Working with Keras, TensorFlow, gensim, and scikit-learn 
  • Rule-based and data-based NLP 
  • Scalable pipelines 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wants to know applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries—all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills.  
  • This course requires a basic understanding of deep learning and intermediate Python skills. 
  • 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 

 

  1.  
  1. Packets of thought (NLP overview) 
  • Natural language vs. programming language 
  • The magic 
  • Practical applications 
  • Language through a computer’s “eyes” 
  • A brief overflight of hyperspace 
  • Word order and grammar 
  • A chatbot natural language pipeline 
  • Processing in depth 
  • Natural language IQ 
  1. Build your vocabulary (word tokenization) 
  • Challenges (a preview of stemming) 
  • Building your vocabulary with a tokenizer 
  • Sentiment 
  1. Math with words (TF-IDF vectors) 
  • Bag of words 
  • Vectorizing 
  • Zipf’s Law 
  • Topic modeling 
  1. Finding meaning in word counts (semantic analysis) 
  • From word counts to topic scores 
  • Latent semantic analysis 
  • Singular value decomposition 
  • Principal component analysis 
  • Latent Dirichlet allocation (LDiA) 
  • Distance and similarity 
  • Steering with feedback 
  • Topic vector power 
  1. Baby steps with neural networks (perceptron’s and backpropagation) 
  • Neural networks, the ingredient list 
  1. Reasoning with word vectors (Word2vec) 
  • Semantic queries and analogies 
  • Word vectors 
  1. Getting words in order with convolutional neural networks (CNNs) 
  • Learning meaning 
  • Toolkit 
  • Convolutional neural nets 
  • Narrow windows indeed 
  1. Loopy (recurrent) neural networks (RNNs) 
  • Remembering with recurrent networks 
  • Putting things together 
  • Let’s get to learning our past selves 
  • Hyperparameters 
  • Predicting 
  1. Improving retention with long short-term memory networks 
  • LSTM 
  1. Sequence-to-sequence models and attention 
  • Encoder-decoder architecture 
  • Assembling a sequence-to-sequence pipeline 
  • Training the sequence-to-sequence network 
  • Building a chatbot using sequence-to-sequence networks 
  • Enhancements 
  • In the real world 
  1. Information extraction (named entity extraction and question answering) 
  • Named entities and relations 
  • Regular patterns 
  • Information worth extracting 
  • Extracting relationships (relations) 
  • In the real world 
  1. Getting chatty (dialog engines) 
  • Pattern-matching approach 
  • Grounding 
  • Retrieval (search) 
  • Generative models 
  • Four-wheel drive 
  • Design process 
  • Trickery 
  • In the real world 
  1. Scaling up (optimization, parallelization, and batch processing) 
  • Too much of a good thing (data) 
  • Optimizing NLP algorithms 
  • Constant RAM algorithms 
  • Parallelizing your NLP computations 
  • Reducing the memory footprint during model training 
  • Gaining model insights with Tensor Board 

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?