Paco Nathan

Using the Python API for Senzing to run entity resolution on three datasets about businesses in the Las Vegas metro area: SafeGraph, WHISGARD wage compliance from US Dept of Labor, PPP loans from US Chamber of Commerce. We build a knowledge graph in Neo4j from the results, then use Jupyter, Pandas, Seaborn, PyVis to compare the before/after of resolving duplicate records.

A f*ck-around-and-find-out whodunit tale of neo-noir gore and messy flip-the-script cli-fi about artificial intelligence, animism, national security liberals, insurrection, climate guilt, weaponized media, advanced mathematics, conspiracism, global cyberwar, overlapping polycrisis, and the strangest of bedfellows.

In this liveProject, you’re a data scientist at a healthcare provider that deals with large volumes of incoming text. Your task is to analyze a large dataset containing medical transcriptions. Leveraging technologies including pandas, the IBM Project Debater API, and Seaborn, you’ll explore a Kaggle dataset, segment text data into known categories, and extract key points.

You’ll finish by building an interactive data visualization dashboard for analysis in the open-source framework… Show more

In this liveProject, you’re a data scientist at a healthcare provider that deals with large volumes of incoming text. Your task is to analyze a large dataset containing medical transcriptions. Leveraging technologies including pandas, the IBM Project Debater API, and Seaborn, you’ll explore a Kaggle dataset, segment text data into known categories, and extract key points.

You’ll finish by building an interactive data visualization dashboard for analysis in the open-source framework Streamlit. When you’re done, you’ll have leveled up your NLP toolbox with skills that are highly sought not only in healthcare but in law, customer support, market intelligence, media, and many other fields. Show less

Applications of AI in Healthcare ​pose a number of challenges and considerations which differ substantially from other business verticals. We conducted an industry survey specifically about AI in healthcare, to understand more about current trends and issues. A total of 321 respondents from 41 countries participated in the survey. A quarter of all respondents (27%) held ​Technical Leadership​ roles. This survey was conducted in collaboration with John Snow Labs.

Building, deploying, and optimizing recommender systems that effectively engages users and impacts business value, including revenue, is hard. Data scientists, machine learning engineers, and leads within global e-commerce, media, and on-demand domains have successfully designed, built, and deployed recommendation systems that impact business value. Download this paper to get insights, best practices, and advice from expert interviews and uncover how recommender systems teams handle… Show more

Building, deploying, and optimizing recommender systems that effectively engages users and impacts business value, including revenue, is hard. Data scientists, machine learning engineers, and leads within global e-commerce, media, and on-demand domains have successfully designed, built, and deployed recommendation systems that impact business value. Download this paper to get insights, best practices, and advice from expert interviews and uncover how recommender systems teams handle preprocessing, feature engineering, training models, evaluating models, selecting which appropriate technologies to integrate, interoperability with open source, and more. Learn insights from leaders and technical experts at global companies such as The New York Times, Tencent, Meituan, NVIDIA, and more. Show less

Our 2021 NLP Industry Survey report is informed by several important contrasts: organizations with years of history deploying NLP applications in production compared to those which are exploring NLP, responses from Technical Leaders versus general practitioners, and company size. We draw insights and indicate trends based on those contrasts. This survey was conducted in collaboration with John Snow Labs.

Graph Thinking, as a cognitive framework for approaching complex analytics problems which can be solved with graph technologies – with analogies from learning theory, about how people organize knowledge in graph-like cognitive structures as they progress from novice to expert in a given field.

Key features of ML monitoring solutions, why companies need a holistic MLOps platform that includes model monitoring, and challenges companies face in making that happen.

Learn why hardware innovations demand rethinking how data teams build analytics and ML applications.

Applications of AI in Healthcare ​pose a number of challenges and considerations which differ substantially from other business verticals. We conducted an industry survey specifically about AI in healthcare, to understand more about current trends and issues. A total of 373 respondents from 49 countries participated in the survey. A quarter of all respondents (27%) held ​Technical Leadership​ roles. This survey was conducted in collaboration with John Snow Labs.

Across industry sectors, both management and leaders see a yawning gap between the promised and delivered impact of data science projects and wonder why the discrepancy exists. It's simple, really. Companies rely on highly skilled and expensive data scientists to help them build predictive capabilities into their products and workflows, but they often think the data science team alone can lead the change.

This report examines issues from several conversations the authors held with data… Show more

Across industry sectors, both management and leaders see a yawning gap between the promised and delivered impact of data science projects and wonder why the discrepancy exists. It's simple, really. Companies rely on highly skilled and expensive data scientists to help them build predictive capabilities into their products and workflows, but they often think the data science team alone can lead the change.

This report examines issues from several conversations the authors held with data science teams across industries, as well as those issues they've witnessed in their own experience as builders and leaders. Among their findings, the authors agreed that to shorten the production process, lower overhead, and reduce risk, organizations need a comprehensive understanding of how to build AI in a repeatable fashion. Show less

The Natural Language Processing (NLP) Industry Survey was an online survey which ran for 41 days (July 5 to August 14, 2020). A total of 571 respondents from more than 50 countries completed the survey. A quarter of all respondents hold technical leadership roles. Respondents were recruited via social media, online advertising, the Gradient Flow Newsletter, and through industry partners and contacts. This survey was sponsored by John Snow Labs.

RLlib is an open-source library in Python, based on Ray, which is used for reinforcement learning (RL). This article provides a hands-on introduction to RLlib and reinforcement learning by working step-by-step through sample code. The material in this article, which comes from Anyscale Academy, provides a complement to the RLlib documentation.

Open source tools and techniques for visualizing data on custom maps in Python.

This ground-breaking book explores how automating the search for and discovery of datasets can help tackle irreproducibility in social science.

As more companies work to adopt AI for business processes, project costs and failure rates are on the rise. Why? No standard practice exists for implementing AI in business applications, and many organizations don’t have the skills, processes, and tools to mitigate risk.

Every decade since the 1960s, researchers at companies like IBM, Amazon, and many others have introduced major new frameworks and techniques to handle rising data management problems. This concise ebook explains how these new systems helped data science evolve quickly—from hierarchical and relational databases to big data and cloud computing to streaming and graph data.

What are the open source libraries in Python which are popularly used in data science work, and how do they fit together?

While O’Reilly has identified several trends among enterprise companies for adopting artificial intelligence, we decided to drill down further to learn just how businesses worldwide are planning and prioritizing this work. In a recent survey, we asked respondents about revenue-bearing AI projects their organizations have in production. How might their AI adoption patterns change over the course of the next year?

How are companies using or exploring AI, big data, and the cloud for advanced analytics and automation? In an O’Reilly survey conducted in October 2018, more than 3,200 companies throughout the world—located primarily in North America, Europe, and Asia—revealed their choices of tools, technologies, and practices for pursuing sophisticated cloud-based data solutions.

While the use of machine learning (ML) in production started near the turn of the century, it’s taken roughly 20 years for the practice to become mainstream throughout industry. With this report, you’ll learn how more than 11,000 data specialists responded to a recent O’Reilly survey about their organization’s approach—or intended approach—to machine learning.

Imagine cooking a stew with a single ingredient or growing a country garden with a single type of flower. One-dimensional efforts like these yield bland and boring results. Now imagine staffing a data science team with only PhDs in machine learning. In spite of the impressive pedigree, the result would be similar: bland, boring, and, possibly worse, ineffective.

But if not just data people, then who?

With its ability to perform fast, in-memory cluster computing, Apache Spark is emerging as a favorite technology for analytics on large datasets. This video workshop from Paco Nathan (host of the Just Enough Math workshop) provides developers with an introduction to Spark and its core APIs. By working with hands-on technical exercises, you’ll get up to speed on how to use Spark for data exploration, analysis, and building big data applications in Python, Java, or Scala.

The webcast introduces advanced math for business people — "just enough" to take advantage of open source frameworks — including graph theory, abstract algebra, optimization, bayesian statistics, and more advanced areas of linear algebra. These are needed for supply chain optimization, pricing models, and anti-fraud, especially given the increased data rates coming from the Internet of Things.

Authored a full-day, hands-on workshop introducing Apache Spark, led team + partners to deliver instruction worldwide.

How can data be leveraged to make food production and distribution systems more responsive, resilient, and efficient? An ecosystem of agricultural data has been quietly evolving, and is rapidly becoming a vital component of global food security. The data rates and variety are vast: remote sensing via small satellites, sensor networks in the fields, tractors­-as-­drones, and more. Many issues implied by this category of data, however, are quite subtle and in some cases counter­intuitive. Given… Show more

How can data be leveraged to make food production and distribution systems more responsive, resilient, and efficient? An ecosystem of agricultural data has been quietly evolving, and is rapidly becoming a vital component of global food security. The data rates and variety are vast: remote sensing via small satellites, sensor networks in the fields, tractors­-as-­drones, and more. Many issues implied by this category of data, however, are quite subtle and in some cases counter­intuitive. Given that this field is relatively new and not particularly organized yet, key learnings may be adapted from other sectors where large-­scale data and analytics have already played a transformational role: finance, intelligence, e-­commerce, telecom, energy, etc. Show less

Despite its growing use in the enterprise, building applications for Hadoop is notoriously difficult. But there is a solution. This hands-on book introduces you to Cascading, the framework that enables you to build powerful data processing applications on Hadoop without having to spend months learning the intricacies of MapReduce.

Whether you’re a developer, data scientist, or system/IT administrator, you’ll quickly learn Cascading’s streamlined approach to data processing, data… Show more

Despite its growing use in the enterprise, building applications for Hadoop is notoriously difficult. But there is a solution. This hands-on book introduces you to Cascading, the framework that enables you to build powerful data processing applications on Hadoop without having to spend months learning the intricacies of MapReduce.

Whether you’re a developer, data scientist, or system/IT administrator, you’ll quickly learn Cascading’s streamlined approach to data processing, data filtering, and workflow optimization, using sample apps based on Java, Scala, and Clojure. Companies such as Etsy, Razorfish, TeleNav, and Twitter already use Cascading for mission-critical applications. This book shows you how this framework can help your organization extract meaningful information from large amounts of distributed data. Show less

Building a case for use of risk metrics in determining reasonable countermeasures to network security attacks. Introduction to "OpenSIMS" open source project.

Review of "corporate metabolism" metaphor.

An extensive analysis of the structure and function of the "corporate organism".

Coverage of federal court case in Steve Jackson Games vs. US Secret Service.