U&P AI – Natural Language Processing (NLP) with Python

Natural Language Processing

U&P AI – Natural Language Processing (NLP) with Python

Become an NLP Engineer by creating real projects using Python, semantic search, text mining and search engines!

What you’ll learn

  • Understand every detail and build real stuff in NLP
  • (NEW)Learn how some plugins use semantic search to generate source code
  • (NEW)Building your vocabulary for any NLP model
  • (NEW)Reducing Dimensions of your Vocabulary for Machine Learning Models
  • (NEW)Feature Engineering and convert text to numerical values for machine learning models
  • (NEW) Keyword search VS Semantic search
  • (NEW)Similarity between documents
  • (NEW)Dealing with WordNet
  • (NEW)Search engines under the hood
  • Tokenizing text data
  • Converting words to their base forms using stemming
  • Converting words to their base forms using lemmatization
  • Dividing text data into chunks
  • Dealing with corpuses
  • Extracting document term matrix using the Bag of Words model
  • Building a category predictor
  • Constructing a gender identifier
  • Building a sentiment analyzer
  • Topic modeling using Latent Dirichlet Allocation


  • A little bit of python


In this course, we are going to learn about natural language processing. We will discuss various concepts such as tokenization, stemming, and lemmatization to process text. We will then discuss how to build a Bag of Words model and use it to classify text. We will see how to use machine learning to analyze the sentiment of a given sentence. We will then discuss topic modeling and implement a system to identify topics in a given document. We will start with simple problems in NLP such as Tokenization Text, Stemming, Lemmatization, Chunks, Bag of Words model. and we will build some real stuff such as :

  1. Learning How to Represent the Meaning of Natural Language Text
  2. Building a category predictor to predict the category of a given text document.
  3. Constructing a gender identifier based on the name.
  4. Building a sentiment analyzer used to determine whether a movie review is positive or negative.
  5. Topic modeling using Latent Dirichlet Allocation
  6. Feature Engineering Natural Language Processing 
  7. Dealing with corpora and WordNet Natural Language Processing 
  8. Dealing With your Vocabulary for any NLP and ML model

Who this course is for:

  • Anyone who wants to understand NLP concepts and build some projects
  • Beginner python developers curios about NLP, this course is not for experienced data scientists

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