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Presentation: Practical NLP for the Real World

Track: Sequential Data: Natural Language, Time Series, and Sound

Location: Cyril Magnin I

Duration: 9:00am - 9:40am

Day of week: Wednesday

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Most companies have an abundance of text data they could leverage information from. Whether you'd like to classify incoming support ticket requests, detect when a candidate is available for an interview or suggest useful replies to emails, leveraging NLP techniques can often improve your products and allow you to build entirely new ones.

The field of NLP is often not the most approachable, however. The field ranges from linguistics to cutting edge deep learning, so it can be hard to find tools to build a practical product in a reasonable timeframe.

In this talk, we will cover concrete examples of how to build practical applications using NLP. In the real world, most gains come from improvements to the pipeline, not necessarily the model. For this reason, we will dive into data visualization and labelling, as well as model validation.

We will walk through code, plots, and leave ample room for practical questions.

Speaker: Emmanuel Ameisen

Head of AI @InsightDataSci

Emmanuel Ameisen is the Head of AI at Insight Data Science. Emmanuel has years of experience going from product ideation to effective implementations. At Insight, he has led over a hundred AI projects from ideation to finished product in a variety of domains including Computer Vision, Natural Language Processing, and Speech Processing. Previously, he implemented and scaled out predictive analytics and machine learning solutions for Local Motion and Zipcar. Emmanuel holds master’s degrees in artificial intelligence, computer engineering, and management from three of France’s top schools.

Find Emmanuel Ameisen at

2019 Tracks

  • Predictive Data Pipelines & Architectures

    Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix

  • Sequential Data: Natural Language, Time Series, and Sound

    Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud