Leonardo De Marchi
Head of Data Science and Analytics | Badoo
Understand NLP basics
Create an NLP pipeline to preprocess the data using Python
Perform topic modelling
Use Python libraries for NLP tasks, in particular NLTK, Gensim and Glove
Leverage transfer learning and text embeddings to perform NLP classification
Overview of BERT and ELMo
Extracting knowledge from text data has always been one of the most researched topics in machine learning, but only recently we witnessed breakthroughs that put NLP in the spotlight. Many pieces of information are stored in unstructured data, like text, which is extremely important in many different fields, from finance to social media and e-commerce.
In this course we will go through Natural Language Processing fundamentals, such as pre-processing techniques,tf-idf, embeddings, and more. It will be followed by practical coding examples, in python, to teach how to apply the theory to real use cases.
The goal of this workshop is to provide the attendees all the basic tools and knowledge they need to solve real problems and understand the most recent and advanced NLP topics.
Lesson 1 Text Representation (60m)
Theory: Familiarize yourself with NLP fundamentals and text preprocessing, to prepare the data for our models. We will go through the main steps like removing stopwords, stemming, One-Hot Encoding, and more.
Exercise: Apply text preprocessing methods on a simple dataset.
Outcome: You will be able to apply to the appropriate methodology to preprocess the text.
Lesson 2 Topic Modeling (45m)
Theory: We will see what LDA is and how it can help to extract information from documents. We will also try different clustering techniques and implement a Non-negative Matrix factorization.
Exercise: Apply topic modeling techniques on a simple text.
Outcome: You will be able to apply to extract the main information from documents using topic modeling techniques.
Lesson 3 Text Classification (30m)
Theory: We will learn how it’s possible to represent text and how a classifier can use this representation. We will use TF-Idf and experiment with a couple of supervised learning models.
Exercise: Build an NLP pipeline to perform classification.
Outcome: You will be able to solve a text classification problem end to end.
Lesson 4 Introduction to Deep Learning in NLP (45m)
Theory: Understand word embedding, how it works, and how to use it. We will go through the main concepts behind word embedding and see some practical examples using the Gensim library.
Exercise: Leveraging python deep learning libraries to create an NLP pipeline for sentiment analysis.
Outcome: You will be able to use word embedding to perform any text classification task.
Lesson 5 Overview of Advanced Deep NLP (15m)
We will introduce the most recent development of Deep learning in NLP, in particular we will see how to leverage BERT and ELMo and their pre-trained models to solve NLP problems.
This course is designed for data scientists, data analysts and software engineers who want to start working with NLP without treating it like a black box. They want to have an understanding of the theory but most importantly how to approach a real problem.
We will be using python in all exercises therefore some python knowledge is required. Some machine learning knowledge is beneficial but not required. We will introduce all the basic concepts needed but without spending much time on the most basic ML concepts.