O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI In this post, we shed some light on various efforts toward generating data for machine learning (ML) models. Machine learning applications rely on three main components: models, data, and compute.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

IBM Big Data Hub

FEBRUARY 14, 2024

Using machine learning (ML), AI can understand what customers are saying as well as their tone—and can direct them to customer service agents when needed. Customer-facing AI use cases Deliver superior customer service Customers can now be assisted in real time with conversational AI.

Cost-Benefit 90

Cost-Benefit Insurance Unstructured Data Machine Learning 90

IBM Big Data Hub

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data.

Data mining 59

Data mining Machine Learning Deep Learning Marketing 59

datapine

AUGUST 14, 2019

Data science tools are used for drilling down into complex data by extracting, processing, and analyzing structured or unstructured data to effectively generate useful information while combining computer science, statistics, predictive analytics, and deep learning. offers many statistics and machine learning abilities.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

Domino Data Lab

JULY 28, 2021

There are many software packages that allow anyone to build a predictive model, but without expertise in math and statistics, a practitioner runs the risk of creating a faulty, unethical, and even possibly illegal data science application. All models are not made equal. After cleaning, the data is now ready for processing.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

IBM Big Data Hub

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning?

Machine Learning 83

Machine Learning Modeling Deep Learning Predictive Modeling 83

Smarten

APRIL 19, 2024

Benefits include customized and optimized models, data, parameters and tuning. As the foundation for ChatGPT and other developed solutions, GPT can be used for a wide range of tasks and is well-suited for language translation and content creation (as opposed to content repurposing).

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Rocket-Powered Data Science

JULY 13, 2023

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”).

Analytics 166

Analytics Machine Learning Data Science Deep Learning 166

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

CIO Business Intelligence

MARCH 22, 2023

Enter the new class ML data scientists require large quantities of data to train machine learning models. Then the trained models become consumers of vast amounts of data to gain insights to inform business decisions.

Machine Learning 106

Machine Learning Cost-Benefit Modeling Risk 106

Smart Data Collective

MAY 17, 2021

Big data solutions are often created and supported using various technologies from IIoT to machine learning and AI. All that performance data can be fed into a machine learning tool specifically designed to identify certain events, failures or obstacles. It also introduces operational efficiencies.

Big Data 102

Big Data Cost-Benefit IoT Machine Learning 102

Domino Data Lab

APRIL 21, 2021

In this article, we’ll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human eye might not catch. deep learning) there is no guaranteed explainability. Model training. It can be implemented as either unsupervised (e.g.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Alation

APRIL 14, 2022

They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning. Governance influences how an organization’s objectives are set and achieved, how risk is monitored and addressed, and how performance is optimized.” It’s not a simple definition.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Domino Data Lab

JULY 9, 2019

Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. I still believe that data science is the craft of trying to apply machine learning to some real world problem.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena. 40; it peaked at Strata NY 2018 at No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139