IBM Big Data Hub

FEBRUARY 14, 2024

Other uses include Netflix offering viewing recommendations powered by models that process data sets collected from viewing history; LinkedIn uses ML to filter items in a newsfeed, making employment recommendations and suggestions on who to connect with; and Spotify uses ML models to generate its song recommendations.

Cost-Benefit 88

Cost-Benefit Insurance Unstructured Data Machine Learning 88

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 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 58

Data mining Machine Learning Deep Learning Marketing 58

bridgei2i

DECEMBER 15, 2021

Naturally, the change in consumer behavior prompted media companies to change their business models. Imperative to predicting user preferences or interests and suggestions, the recommendation engine market size is projected to reach $12.03 Anticipating Demand through Predictive Modelling on OTT. billion by 2025.[1]

Recreation/Entertainment 52

Recreation/Entertainment Metadata Advertising Predictive Modeling 52

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning in marketing and sales According to Forbes , marketing and sales teams prioritize AI and ML more than any other enterprise department. Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). Antivirus programs may use AI and ML techniques to detect and block malware.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

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

Smarten

APRIL 19, 2024

OpenAI – Azure OpenAI as the foundational entity for creating GPT models and is based on Large Language Models (LLM). GPT – Is based on a Large Language Model (LLM). Benefits include customized and optimized models, data, parameters and tuning. Open AI was developed by Microsoft.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

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

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

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. However, the growing influence of ML isn’t without complications.

Machine Learning 82

Machine Learning Modeling Deep Learning Predictive Modeling 82

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. In the training phase, the primary objective is to use existing examples to train a model.

Machine Learning 105

Machine Learning Cost-Benefit Modeling Risk 105

Domino Data Lab

APRIL 21, 2021

We’ll use a gradient boosting technique via XGBoost to create a model and I’ll walk you through steps you can take to avoid overfitting and build a model that is fit for purpose and ready for production. deep learning) there is no guaranteed explainability. Model training. Oversampling.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Alation

APRIL 14, 2022

And, as organizations progress and grow, “data drift” starts to impact data usage, models, and your business. They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning. Model Drift. Models too, can drift and transform over time.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Smart Data Collective

MAY 17, 2021

All that performance data can be fed into a machine learning tool specifically designed to identify certain events, failures or obstacles. Predictive models, estimates and identified trends can all be sent to the project management team to speed up their decisions. That’s also where big data can step in and vastly expand ops.

Big Data 102

Big Data Cost-Benefit IoT Machine Learning 102

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

O'Reilly on Data

SEPTEMBER 11, 2019

2 in frequency in proposal topics; a related term, “models,” is No. An ML-related topic, “models,” was No. For example, even though ML and ML-related concepts —a related term, “ML models,” (No. Deep learning,” for example, fell year over year to No. 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

Assessing whether a business stakeholder is trying to solve for a problem that is descriptive, predictive, or prescriptive and then re-framing the problem as supervised learning, unsupervised learning, or reinforcement learning, respectively. or a prescriptive model? or a descriptive model?”

Data Science 112

Data Science Machine Learning Advertising Modeling 112