Sisense

APRIL 10, 2020

We’ll actually do this later in this article. These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. Modeling in R and Python. In modeling, we find relationships between input variables and the target variable. R libraries.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Predictive models indicate that the machine learning market will grow at a compound annual growth rate (CAGR) of 38.8%

Data Governance 52

Data Governance Marketing Machine Learning Sales 52

Alation

APRIL 25, 2023

As firms mature their transformation efforts, applying Artificial Intelligence (AI), machine learning (ML) and Natural Language Processing (NLP) to the data is key to putting it into action quickly and effecitvely. Predictive models indicate that the machine learning market will grow at a compound annual growth rate (CAGR) of 38.8%

Data Governance 40

Data Governance Marketing Machine Learning Sales 40

Smarten

APRIL 19, 2024

In this article, we will discuss the current state of AI in analytics, as well as the future of this burgeoning industry and how it can be applied to analytics to simplify and clarify results and to make analytics easier for businesses and business users to leverage.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Insight

MARCH 18, 2019

Bias in Machine Learning Algorithms (Bottom Photos Source: ProPublica ; Top Photos Source: Pexels.com) Biases in predictive modeling are a widespread issue Machine learning and AI applications are used across industries, from recommendation engines to self-driving cars and more.

Machine Learning 77

Machine Learning Predictive Modeling Modeling Metrics 77

Rocket-Powered Data Science

JULY 6, 2021

1) Automated Narrative Text Generation tools became incredibly good in 2020, being able to create scary good “deep fake” articles. 2) MLOps became the expected norm in machine learning and data science projects. the monitoring of very important operational ML characteristics: data drift, concept drift, and model security).

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

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. from sklearn import metrics. It can be implemented as either unsupervised (e.g. from imblearn.over_sampling import SMOTE.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

CIO Business Intelligence

AUGUST 15, 2023

And it yields multiple business metric improvements, such as limiting surplus inventory. The Behavioral Health Acuity Risk (BHAR) model leverages a machine learning technique called random forests, which can be natively hosted in the electronic health record and updated in near-real time, with results immediately available to clinical staff.

IT 98

IT Manufacturing IoT Cost-Benefit 98

datapine

MARCH 25, 2022

While some experts try to underline that BA focuses, also, on predictive modeling and advanced statistics to evaluate what will happen in the future, BI is more focused on the present moment of data, making the decision based on current insights. Your Chance: Want to extract the maximum potential out of your data?

Business Intelligence 264

Business Intelligence Analytics Statistics Dashboards 264

IBM Big Data Hub

JULY 5, 2023

In this article, we’ll take a look at some best practices that successful businesses use to care for their assets and extend their useful lives. Predictive maintenance constantly assesses and re-assesses an asset’s condition in real-time via sensors that collect data via IoT.

Strategy 58

Strategy Management IoT Cost-Benefit 58

Sisense

SEPTEMBER 25, 2020

In this article, we will explain how to execute five statistical techniques using Python. Interpreting better results: Statistical techniques allow users to make predictions for unseen data, more easily improving the accuracy of output and results. Linear regression is a form of supervised learning (or predictive modeling).

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

Domino Data Lab

OCTOBER 23, 2019

This article provides insight on the mindset, approach, and tools to consider when solving a real-world ML problem. One of the things this data set doesn’t have, however, is a single metric to rank both offensive and defensive performance in a single statistic. Unsupervised Machine Learning: Clustering First Data Sources.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Domino Data Lab

AUGUST 22, 2019

Note These three particular words are called articles , or determiners. With the SG architecture, context words are predicted given the target word. With CBOW, it is the inverse: The target word is predicted based on the context words. Journal of Machine Learning Research, 9, 2579–605.]. Example 11.9 0.85 = 0.15.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

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

datapine

JANUARY 31, 2022

Renowned author Bernard Marr wrote an insightful article about Shell’s journey to become a fully data-driven company. 5) Find improvement opportunities through predictions. BI dashboards like the one presented below provide a centralized view of the most important metrics businesses need to stay ahead of their competitors.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

datapine

NOVEMBER 27, 2019

We detailed the benefits and costs of good or bad quality data in our previous article on data quality management , where you can read the five important pillars to follow. Tech giants use AI in many different ways that will alternate the machine learning process and we should keep an eye on this process in 2020.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

datapine

DECEMBER 4, 2019

Predictive & Prescriptive Analytics. Predictive Analytics: What could happen? We mentioned predictive analytics in our business intelligence trends article and we will stress it here as well since we find it extremely important for 2020. Share the essential business intelligence buzzwords among your team!

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

Smarten

DECEMBER 3, 2023

This article will help you gather information about the topic of augmented analytics. Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms.

Key Performance Indicator 52

Key Performance Indicator Analytics IT Predictive Analytics 52

Jet Global

MAY 1, 2023

As a result, end users can better view shared metrics (backed by accurate data), which ultimately drives performance. When treating a patient, a doctor may wish to study the patient’s vital metrics in comparison to those of their peer group. They can also create custom calculations and metrics, and build new data visualizations.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52