NPL – Natural Language Processing Fundamentals


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Tulevat päivämäärät

Oct 17 - Oct 19, 2022
09:00 - 17:00

Nov 14 - Nov 16, 2022
09:00 - 17:00

Dec 12 - Dec 14, 2022
09:00 - 17:00

Jan 9 - Jan 11, 2023
09:00 - 17:00

Feb 6 - Feb 8, 2023
09:00 - 17:00

Mar 6 - Mar 8, 2023
09:00 - 17:00

NPL – Natural Language Processing Fundamentals
3 days  (Instructor Led Online)  |  Programming

Course Details


This comprehensive Natural Language Processing (NPL) Fundamentals training course will show you how to effectively use Python libraries and NLP concepts to solve various problems. This is a three-day course that starts with basics and goes on to explain various NLP tools and techniques that equip you with all that you need to solve common business problems for processing text.

In this NPL training course, you’ll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modelling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you’ll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you’ll understand how to apply NLP techniques to answer questions as can be used in chatbots.

By the end of this course, you’ll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or genesis for performing sentiment analysis. This NPL training course will easily equip you with the knowledge you need to build applications that interpret human language.



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Lesson 1: Introduction to NLP

  • What is natural language processing (NLP)?
  • Types of natural language processing tasks
  • Structuring a natural language processing project

Lesson 2: Extraction Methods from Unstructured Text

  • Tokenization methods
  • Term frequency observations
  • Bag-of-Words and TF-IDF

Lesson 3: Building a Simple Classifier

  • Basic theoretical coverage and sample code of Supervised and Unsupervised
  • Classifiers vs. regressors
  • Sampling and splitting data for training algorithms
  • Evaluating the performance of a model
  • Use of Pandas and scikit-learn

Lesson 4: Collecting Text Data

  • Retrieve and process web page data using urllib, bs4
  • Handle various types of data such as JSON, XML
  • Retrieve real-time data using API provided by the website

Lesson 5: Topic Modeling

  • Loading and preprocessing documents into a noted course
  • Training an LDA model to detect the topics in the document
  • Visually represent the topics found in a set of documents

Lesson 6: Text Summarization and Text Generation

  • Summarizing document using word frequency
  • Generating random text using the Markov chain
  • Compare the results between recent methods

Lesson 7: Vector Representation

  • Converting words to word vectors
  • Perform math-like operations on word vectors e.g. king – man = queen
  • Converting documents to document vectors.
  • Using document vectors to measure the similarity between documents

Lesson 8: Sentiment Analysis

  • Load a labelled dataset of movie reviews
  • Use word vectors to represent the words in the movie review
  • Train a simple model to predict whether the movie review is positive or negative


Natural Language Processing Fundamentals is designed for novice and mid-level data scientists and machine learning developers who want to gather and analyze text data to build an NLP-powered product. It’ll help you to have prior experience of coding in Python using data types, writing functions, and importing libraries. Some experience with linguistics and probability is useful but not necessary.



This NPL training course will require a computer system for the instructor and one for each student. The minimum hardware requirements are as follows:

  • Processor: Dual Core or better
  • Memory: 4 GB RAM
  • Hard disk: 10 GB
  • Internet connection



  • Operating system: Windows 7 SP1 32/64-bit, Windows 8.1 32/64-bit, Windows 10 32/64-bit, Ubuntu 14.04 or later, or macOS Sierra or later
  • Browser: Google Chrome or Mozilla Firefox
  • Conda
  • Jupyterlab
  • Python 3.x