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Introduction to Natural Language Processing
Natural Language Processing (NLP) allows us to classify, correct, predict, and even translate large text data quantities. In this course, you will discover how to transform text into vectors for exploration and classification. We will explore bag-of-words, word embeddings, and sentiment analysis.Difficile
10 heures
Ce cours en libre accès vous intéresse ?
Natural language processing, otherwise known as NLP, is the technology behind Siri, autocorrect, chatbots, and Google Translate. It’s what helps you translate text, filter spam, and detect fake news. In short, this technology allows a machine to understand and process human language.
But how does it work under the hood? How can you use NLP to transform human language into something a computer can understand? Look no further; this course has the answer!
In Part 1 of this course, we will explore how to preprocess text data and prepare it for further exploitation by a computer.
In Part 2, we will explore a text vectorization technique called bag-of-words and solve text classification problems such as sentiment analysis.
In Part 3, you will learn a more powerful vectorization technique called word embeddings and apply it to infer meaning from a text.
Once you complete this course, you will have a basic understanding of how NLP models work and how to use them in machine-learning projects. We will also introduce you to the spaCy 3.4, scikit-learn 1.1, and NLTK 3.7 libraries in Python 3.10.
Ready to dive into one of the most innovative domains in artificial intelligence? Then let’s get started!
Objectifs pédagogiques
- Preprocess Text Data
- Vectorize Text for Classification Using Bag-of-Words
- Vectorize Text For Exploration Using Word Embeddings
Prérequis
Prerequisites:
To take advantage of this course, you must be familiar with Python 3.10 and be able to use Python libraries to manipulate data. You must also be familiar with basic linear algebra and stats and the main concepts behind machine learning, including scoring and training.
If you are unfamiliar with these concepts, take the following courses:
- Train a Supervised Machine Learning Model
- Learn Python Basics for Data Analytics
- Use Python Libraries for Data Science
Required tools:
- Python 3.10, including:
- spaCy 3.4
- NLTK 3.7
- scikit-learn 1.1
- pandas 1.5
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Table des matières
- Partie 1
Preprocess Text Data
- Partie 2
Vectorize Text for Classification Using Bag-of-Words
- Partie 3
Vectorize Text for Exploration Using Word Embeddings
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