Unlocking Conversations: Hands-On NLP for Real-World Data Mining

Why NLP and Why Now?

As conversations around the world explode in number, the need to make sense of them has become more critical than ever. Think about it: 1.5 billion messages on Slack every week, 300 million daily virtual meetings on Zoom at peak, and 260 million conversations happening on Facebook every day. The sheer scale of this data is astounding. But more than that, these conversations have transformed social platforms into treasure troves of information, offering insights into emerging trends, new associations, and evolving narratives.

NLP

At the workshop, we’ll delve into how to capture, analyze, and gain insights from this data using NLP. Whether you’re looking to spot trends, extract key information, or mine metadata, this session will provide you with the tools and techniques to turn this overwhelming amount of unstructured conversation data into something meaningful.

What You Can Expect

This workshop will be hands-on and highly interactive, featuring three primary components:

Building a Question Classifier: We’ll start with a straightforward model that classifies sentences as questions or non-questions. You’ll see that even seemingly simple tasks can get complex as we deal with language’s natural ambiguity.
Creating a Named Entity Recognition (NER) Model: Next, we’ll move into identifying specific entities within text, such as names, places, and organizations. I’ll show you how to gather, clean, and process data to build a reliable NER model that can extract meaningful information from conversations.
Developing a Voice AI Assistant Demo: We’ll bring it all together by integrating both models into a voice assistant app that uses a RESTful API to process input and return classified and annotated data. This is where you’ll see how these models can work together in a real-world application, adding layers of context and relevance to raw data.

Why Attend?

There are plenty of reasons to be excited about this workshop, but here are a few highlights:

Hands-on Learning: We’ll be coding live! For those that are less technical and/or don’t have their laptop prerequisites, I’ll be using Jupyter notebooks in Google Colab, so everyone can follow along.
Real-World Applications: While many workshops focus on isolated NLP models, we’ll be tackling multiple models and showing how they can be combined for enhanced functionality. It’s a rare opportunity to see how these technologies can be applied in real-world scenarios.
Open Resources: I’ll provide code, data resources, and examples that you can take with you, adapt, and use on your projects. This workshop isn’t just about learning theory—it’s about equipping you with tools you can use.

See You at ODSC!

I’m incredibly excited to share this workshop with you all and to dive into the nitty-gritty of NLP. Whether you’re an experienced data scientist, an NLP enthusiast, or just curious about how these systems work, there will be something for you. Plus, you’ll walk away with new skills and practical examples that can help you build better models and unlock new insights from conversation data.

So, if you’re planning to attend ODSC, be sure to check out this session. You won’t want to miss it!

Workshop Info:
Building Multiple Natural Language Processing Models to Work in Concert Together
Date: Oct 30, 2024
Time: 4:35pm

Mining Conversations: Building NLP Models to Decode the Digital Chatter

Breaking Down Conversations

Data is power, and conversation data is a goldmine waiting to be tapped. In this session, we’ll go step by step through the process of creating and training NLP models that can understand the context and meaning behind messages, whether from video meetings, audio calls, or text conversations.

It all starts with data. We’ll begin by learning how to collect raw conversation data from various sources, such as WebRTC applications like LiveKit. Once collected, the next challenge is preprocessing this data. We’ll explore strategies to clean and prepare text for machine learning pipelines, including noise reduction and tokenization.

Natural Language Processing

Once the data is ready, we’ll develop machine learning models to extract critical information to classify sentences and perform named entity recognition. We’ll cover how to build these models using Python, PyTorch, and other state-of-the-art NLP tools.

This session will highlight live demos, where I’ll showcase how to deploy and integrate these models into workflows for practical applications, such as customer service analysis, social media trend detection, and even compliance monitoring.

Why Attend This Session?

At the end of the session, you’ll walk away with more than just theory – you’ll get access to working code and resources that you can immediately apply to your projects. Whether you’re building conversation analytics tools for a social network or mining customer feedback from virtual meetings, this session is designed to provide actionable takeaways. By understanding how to train NLP models to analyze conversations, you can transform raw data into valuable insights for your organization.

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If you’re interested in learning more about ML and NLP, I invite you to attend my session at the 2024 RTC Conference at Illinois Tech, Building Multiple Natural Language Processing Models to Work In Concert Together on Tuesday, October 8, 2024, at 4:15 PM.

2024 RTC Conference at Illinois Tech

You can use the discount code FFSPKR to get $200 off registration. Don’t miss this opportunity to explore the future of machine learning and NLP – register today and be part of the conversation!

Machine Learning for Good: Training Models for Medical Analysis

The Human Factor in ML-Driven Diagnostics

However, it’s easy to get lost in the jargon and overlook the human element behind this revolution. Yes, algorithms can analyze more data in seconds than a doctor might in a lifetime, but these technologies are not about replacing medical professionals—they are about empowering them.

Every pixel, soundwave, and movement analyzed by an ML model carries real human implications. It represents a person’s struggle, their hope for answers, and, ultimately, their health outcomes. By embracing machine learning, we are giving healthcare professionals the tools they need to better understand, diagnose, and treat patients on an individual level.

first car

The healthcare industry’s adoption of ML, particularly in diagnostics, is reshaping the role of doctors from solitary decision-makers to orchestrators of advanced technological tools. The naysayers will paint a picture of a world where machines (or, in today’s terms… AI) will replace humans, but this shouldn’t scare us from embracing these tools. History has shown us that when these new technologies enter our lives, the work doesn’t disappear; it transforms into something new. After all, people said the same thing about automobiles and computers, and look at how that turned out.

Machine Learning Using Multi-Modal Data

What makes this moment even more exciting is the use of multi-modal data—combining information from multiple sources like audio, video, and images. For example, in Parkinson’s Disease diagnosis, an ML model can analyze a patient’s voice, capturing the smallest vocal tremors that may signal early-stage neurodegenerative changes. Simultaneously, video footage of the patient’s movements can be analyzed for physical symptoms, such as tremors or rigidity, that might otherwise go unnoticed in a short clinical visit.

This holistic view of patient data allows for more comprehensive and nuanced diagnoses. It’s not just about analyzing a static image or isolated metric but about building a complete narrative from diverse data sources. These advanced models can sift through the noise and detect meaningful patterns across multiple channels of information, dramatically improving early diagnosis and treatment options.

The Future: Empowering Professionals and Patients

The future of ML in Healthcare isn’t just about technical prowess. It’s about how we as a society choose to harness this power. The goal is not to create a future where machines replace human doctors but one where they augment the capabilities of medical professionals, allowing them to provide more personalized and effective care.

science and technology in medicine

Moreover, these advancements don’t just benefit the healthcare providers. Patients themselves stand to gain significantly, with more accurate diagnoses, earlier interventions, and a more involved role in managing their health. With open access to ML tools and resources, professionals from all backgrounds can build tailored recognition solutions that address their specific needs. The future is about democratizing access to these powerful tools, ensuring more people can benefit from the next wave of medical innovation.

With every new advancement, we’re reminded that this isn’t just about technology—it’s about people. The most exciting part of this journey is how ML is transforming Healthcare not just by numbers and codes, but by improving lives, one model at a time.

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If you’re interested in learning more about how ML can revolutionize Healthcare, I invite you to attend our Keynote at the 2024 RTC Conference at Illinois Tech, “Training Machine Learning Classification Models for Creating Real-Time Data Points of Medical Conditions,” on Tuesday, October 8, 2024, at 2:45 PM. Dr. Nikki-Rae Alkema, PT, DPT and I will discuss actionable insights into applying ML models in Healthcare with a live demonstration.