IBM Big Data Hub

AUGUST 28, 2023

Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictive model from the training inputs. Topic modeling: Topic modeling aims to discover underlying themes and/or topics in a collection of documents.

Data mining 58

Data mining Machine Learning Deep Learning Marketing 58

Ms SQL Girl

MAY 19, 2016

To simplify, “In-database Advanced Analytics”: you can run powerful statistical / predictive modelling (from R) inside SQL Server. R Tools for Visual Studio : tools for developing R script in Visual Studio. R Tools for Visual Studio : tools for developing R script in Visual Studio.

Visualization 45

Visualization Predictive Modeling Statistics Reporting 45

Ms SQL Girl

MAY 19, 2016

To simplify, “In-database Advanced Analytics”: you can run powerful statistical / predictive modelling (from R) inside SQL Server. R Tools for Visual Studio : tools for developing R script in Visual Studio. R Tools for Visual Studio : tools for developing R script in Visual Studio.

Visualization 40

Visualization Predictive Modeling Statistics Reporting 40

O'Reilly on Data

DECEMBER 12, 2019

Security vulnerabilities : adversarial actors can compromise the confidentiality, integrity, or availability of an ML model or the data associated with the model, creating a host of undesirable outcomes. The study of security in ML is a growing field—and a growing problem, as we documented in a recent Future of Privacy Forum report. [8].

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

AWS Big Data

MARCH 7, 2023

Refer to the respective service documentation for further details. It enables you to create interactive dashboards, visualizations, and advanced analytics with ML insights. It helps you build, train, and deploy models consuming the data from repositories in the data hub.

Analytics 68

Analytics Data Warehouse Data Lake Metadata 68

Smart Data Collective

SEPTEMBER 26, 2021

As data sets become bigger, it becomes harder to visualize information. Data visualization enables you to: Make sense of the distributional characteristics of variables Easily identify data entry issues Choose suitable variables for data analysis Assess the outcome of predictive models Communicate the results to those interested.

Metadata 100

Metadata Visualization Unstructured Data Data mining 100

O'Reilly on Data

JANUARY 17, 2023

Building Models. A common task for a data scientist is to build a predictive model. You’ll try this with a few other algorithms, and their respective tuning parameters–maybe even break out TensorFlow to build a custom neural net along the way–and the winning model will be the one that heads to production.

Machine Learning 325

Machine Learning Predictive Modeling Software Modeling 325

CIO Business Intelligence

MARCH 5, 2024

Summarizing meetings, emails, and documents Microsoft 365 will set the benchmark on copilot capabilities, given the speed at which it introduced embedded AI assistant features and the platform’s wide user base. Today, top AI-assistant capabilities delivering results include generating code, test cases, and documentation.

Cost-Benefit 121

Cost-Benefit Advertising Experimentation Software 121

Sisense

SEPTEMBER 25, 2020

No matter your skill, career level, or title, the ability to analyze, organize, and visualize data are vital skills in our world of quickly growing and ever-changing data. Linear regression is a form of supervised learning (or predictive modeling). The aim is to create a robust model that avoids both high variance and high bias.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

IBM Big Data Hub

DECEMBER 20, 2023

Unsupervised machine learning Unsupervised learning algorithms—like Apriori, Gaussian Mixture Models (GMMs) and principal component analysis (PCA)—draw inferences from unlabeled datasets, facilitating exploratory data analysis and enabling pattern recognition and predictive modeling.

Machine Learning 80

Machine Learning Modeling Deep Learning Predictive Modeling 80

Domino Data Lab

AUGUST 12, 2021

In a previous blog , we have covered how Pandas Profiling can supercharge the data exploration required to bring our data into a predictive modelling phase. It enables us to build context around the data at hand and lets us develop appropriate models that then can be interpreted correctly. The purpose of Data Exploration.

Machine Learning 126

Machine Learning Reporting Statistics Visualization 126

DataRobot Blog

JANUARY 10, 2023

Now that we have set up the notebook environment, let’s explore the capabilities available in DataRobot Notebooks while walking through the process of training and deploying a readmissions prediction model end-to-end. Built-in Code Snippets and Interactive Visualizations to Accelerate Experimentation. Did you notice?

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

DataRobot Blog

DECEMBER 1, 2022

For example, you could set up a data pipeline that delivers DataRobot predictions to HubSpot to automatically initiate offers within the business rules you set. You could also use the predictions to visualize a BI dashboard or report for your marketing managers to access. Training and Testing Different AI Models.

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

Alation

APRIL 14, 2022

Visual Profiling. They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning. These factors risk data originating in far-flung environments, where the data structures and semantics are not well understood or documented. Parametrization. Pattern Matching. Scheduling.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Occam's Razor

NOVEMBER 18, 2014

Like a vast majority on planet Earth, I love data visualizations. A day-to-day manifestation of this love is on my Google+ or Facebook profiles where 75% of my posts are related to my quick analysis and learnings from a visualization. Data visualized is data understood. But for a visual person like me, this is the ah-ha moment.

Visualization 143

Visualization Slice and Dice Predictive Modeling Metrics 143

Darkhorse

JULY 6, 2017

Let’s further pretend you’re starting out with the aim of doing a big predictive modeling thing using machine learning. Once you’ve documented and understood your sources, you have reached the second stage: data sloughs (note, you have more than one). Proceed visually. I hope you enjoy that sort of thing.

Data Lake 74

Data Lake Sales Machine Learning Visualization 74

Domino Data Lab

OCTOBER 23, 2019

A further diagnostic step is to plot the predicted values of the linear regression versus the actual values. an lmplot of the predicted and actual is shown, and it is obvious that this isn’t that great a prediction model. R has yet one more way to visualize these relationships in multiple dimensions.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Depict Data Studio

SEPTEMBER 17, 2019

In my sixth year of self-employment, the demand for data visualization skills is stronger than ever. This training program is about classic data visualization principles? advanced techniques like applying data visualization principles to reports, slideshows, infographics, and dashboards. What’s Included. We’ll go broad?

Visualization 40

Visualization Consulting Dashboards Reporting 40

O'Reilly on Data

SEPTEMBER 11, 2019

Our call for speakers for Strata NY 2019 solicited contributions on the themes of data science and ML; data engineering and architecture; streaming and the Internet of Things (IoT); business analytics and data visualization; and automation, security, and data privacy. If anything, this focus has shifted to the ML or predictive model.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Domino Data Lab

JULY 9, 2019

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Domino Data Lab

AUGUST 22, 2019

There are steps you can take to preprocess natural language data such that the modeling you carry out downstream may be more accurate. Common natural language preprocessing options include: Tokenization: This is the splitting of a document (e.g., Execute gutenberg.fileids() to print the names of all 18 documents.) Example 11.9

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Rocket-Powered Data Science

SEPTEMBER 20, 2021

We would be able to go far beyond searching for correctly spelled column headings in databases or specific keywords in data documentation, to find the data we needed (assuming we even knew the correct labels, metatags, and keywords used by the dataset creators).

Data Science 285

Data Science Forecasting Business Intelligence Sales 285

Domino Data Lab

AUGUST 20, 2019

Visually, the shape has relatively flat sides. Some of the benefits of rescaling become more prominent when we move beyond predictive modeling and start making statistical or causal claims. We may want to visualize the relationship between the encoding and the original species values. Discretization.

Testing 68

Testing Modeling Interactive Measurement 68

Domino Data Lab

AUGUST 1, 2021

In this article we’ll use Skater , a freely available framework for model interpretation, to illustrate some of the key concepts above. Skater provides a wide range of algorithms that can be used for visual interpretation (e.g. layer-wise relevance propagation), model distillation (e.g. A12 : 0 <= … < 200 DM.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

datapine

NOVEMBER 27, 2019

Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2) Data Discovery/Visualization. Data exploded and became big.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Smarten

FEBRUARY 13, 2022

Out-of-the-box mobile business intelligence provides easy-to-use report formats to create non-columnar, custom reports and templates and design and configure documents that suit the needs of the business.

Analytics 52

Analytics Key Performance Indicator Business Intelligence ROI 52

Jet Global

MAY 1, 2023

Plus, there is an expectation that tools be visually appealing to boot. In the past, data visualizations were a powerful way to differentiate a software application. Their dashboards were visually stunning. Today, free visualizations seem to be everywhere. It’s all about context. End users expect more from analytics too.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

SEPTEMBER 1, 2023

Data-Driven Decision Making: Embedded predictive analytics empowers the development team to make informed decisions based on data insights. By integrating predictive models directly into the application, developers can provide real-time recommendations, forecasts, or insights to end-users.

Predictive Analytics 52

Predictive Analytics Risk Analytics Software 52