Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

Predictive Analytics 121

Predictive Analytics Analytics Predictive Modeling Data mining 121

CIO Business Intelligence

JUNE 7, 2022

Predictive analytics applies techniques such as statistical modeling, forecasting, and machine learning to the output of descriptive and diagnostic analytics to make predictions about future outcomes. Generally, the output of data analytics are reports and visualizations. Data analytics vs. business analytics.

Data Analytics 132

Data Analytics Diagnostic Analytics Management Analytics 132

Sisense

APRIL 10, 2020

It’s quite popular for its visualizations: charts, graphs, pictures, and various plots. These visualizations are useful for helping people visualize and understand trends , outliers, and patterns in data. These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

datapine

AUGUST 14, 2019

To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations. provides the user with visualizations, code editor, and debugging. connecting data sources and predicting future outcomes. Let’s get started.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

IBM Big Data Hub

JULY 6, 2023

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining. Python is the most common programming language used in machine learning.

Machine Learning 57

Machine Learning Data Science Statistics Deep Learning 57

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning. Ribeiro, M.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

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

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. And with advanced software like IBM Watson Assistant , social media data is more powerful than ever.

Data mining 56

Data mining Machine Learning Deep Learning Marketing 56

bridgei2i

DECEMBER 15, 2021

Imperative to predicting user preferences or interests and suggestions, the recommendation engine market size is projected to reach $12.03 Videos uploaded on streaming sites usually include detailed information about the visuals such as descriptions, emotions, and nature of the show. AI in Recommendation Engines for OTT Platforms.

Recreation/Entertainment 52

Recreation/Entertainment Metadata Advertising Predictive Modeling 52

IBM Big Data Hub

FEBRUARY 14, 2024

Creative AI use cases Create with generative AI Generative AI tools such as ChatGPT, Bard and DeepAI rely on limited memory AI capabilities to predict the next word, phrase or visual element within the content it’s generating. AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions.

Cost-Benefit 86

Cost-Benefit Insurance Unstructured Data Machine Learning 86

O'Reilly on Data

DECEMBER 12, 2019

Figure 1 illustrates an example adversarial search for an example credit default ML model. If you’re using Python and deep learning libraries, the CleverHans and Foolbox packages can also help you debug models and find adversarial examples. Interpretable ML models and explainable ML.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

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

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 79

Machine Learning Modeling Deep Learning Predictive Modeling 79

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. An inference algorithm that informs the analyst with a ranked set of suggestions about the transformation. Parametrization. A technique to automate changes in iterative passes.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

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. Deep learning,” for example, fell year over year to No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20