This is a remote course delivered over two half-day sessions via Zoom, for a small group of approx. 10 people. The course introduces you to the implementation of Natural Language Processing applications using the Python programming language and the PyData ecosystem.

Intended audience: Natural Language Processing with Python is aimed at software engineers, data scientists, data analysts, researchers and students who want to get started with Natural Language Processing applications, with the purpose of extracting useful information from free-text data.

The course will balance theoretical foundations with practical examples using the Python programming language. No prior experience with libraries such as NLTK or scikit-learn is required for this course. Having existing experience with Python will be extremely beneficial but not required: users of other programming languages and tools (including e.g. Java, C++, C#, JavaScript, Matlab, Excel or Rlang) will find this course beneficial.

Learning objectives

By attending this course, you will learn about:

• how to choose data representations to effectively work with text data
• exploratory techniques to quickly gain insights from your text data
• how to implement Machine Learning techniques to organise your documents into categories
• how to evaluate the quality of your models
• ideas on advanced applications using Natural Language data

Syllabus Overview

1) NLP Foundations

• Overview on NLP applications and the Python NLP ecosystem
• Working with text: tokenisation, pre-processing, regular expressions
• Word frequencies and co-occurrences: stop-words, Zipf's Law, mining topics of interest with co-occurrences
• Text Representation: n-grams, bag-of-words, word embeddings and document embeddings

2) Topic Modelling

• Bird's-Eye view on a document or dataset
• Navigating topics and sub-topics in a document or dataset

3) Text Classification

• Categorising document and examples of applications (topic classification or sentiment analysis)
• Model evaluation: assessing classification quality
• Model introspection: explaining the classification results

4) Overview on Advanced Applications

An outlook on advanced applications, so attendees are equipped with ideas and techniques to tackle more specific applications. Examples of applications include:

• Named Entity Recognition: identifying named entities in text
• Text Summarisation: extracting the most useful sentences from a document or a collection of documents
• Search Engines: building a search engine to retrieve relevant documents from custom datasets

Course schedule, delivery and teaching style

This course is delivered over two half-day sessions on 9-10 December 2021, 2pm-5:30pm UK time (GMT). We'll have comfort breaks during the sessions as appropriate.

You'll need your computer/laptop with a recent Anaconda pre-installed with Python 3.8+. I'll check-in with you before the course with set-up and joining instructions to make sure you have everything you need.

This is an online only course via Zoom, you'll need an adequate bandwidth for live video conferencing. You can use Zoom via web, but you'd probably have a better experience with the app.

The course is hands-on and offers plenty of demos/examples/exercises: expect to read and write code. Whenever theory is needed, I explain it with a focus on the main intuitions and motivations to keep it practical.

By signing up, you'll receive

• Access to the live sessions on 9-10 December 2021 via Zoom
• Set-up and joining instructions before the course
• Teaching material: code examples, demos and exercises in the form of Jupyter notebooks
• Additional exercises: take-home exercises that you can use after the course to further improve your understanding
• A Certificate of Professional Development after course completion
• An invite for a retrospective group call to discuss progress and problems (1-hour call, circa 2-3 weeks after the training, complimentary)

About the instructor

I'm a Data Science consultant, corporate trainer and author based in London, UK. I specialise in the Python for Data Science (PyData) software stack.

With 20 years of experience in the tech industry, I provide consulting, coaching and training services in the data science space through my company Bonzanini Consulting Ltd.

Backed by a PhD in Information Retrieval, I specialise in text analytics applications, and I’ve enjoyed working on a broad range of information management and data science projects, including flight safety, social media data, behavioural data, biomedical data and recruitment data.

I’m the author of “Mastering Social Media Mining with Python“, “Data Analysis with Python” and “Practical Python Data Science Techniques“, published by Packt Publishing.

I currently serve as chair of the PyData London meetup and conference series (largest Python user group in Europe) and I regularly speak at international tech events.

Refunds and Change Policy

Eventbrite offers you a 100% refund for cancellations up to 7 days before the event. Should you need to cancel I'm happy to talk to you to see if we can figure something out.

I can move a ticket to another colleague if needed, simply get in touch.

In the unlikely event that we have to cancel the course then you will be offered either a 100% refund or the option of moving attendance to another future workshop. Bonzanini Consulting Ltd and Marco Bonzanini are not liable for any other costs incurred.

Questions? Get in touch

If you have questions please contact Marco@BonzaniniConsulting.com.

Check out Marco's blog for a list of past public presentations and workshops. I also curate the newsletter Musings on Data where I share my thoughts and recommendations on Data Science, once every few weeks (it also includes announcements of future trainings, so sign up for early access and discount codes).