IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

Machine Learning 55

Machine Learning Data Science Statistics Deep Learning 55

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

Data Science and Beyond

SEPTEMBER 19, 2016

Some people equate predictive modelling with data science, thinking that mastering various machine learning techniques is the key that unlocks the mysteries of the field. However, there is much more to data science than the What and How of predictive modelling. Making Bayesian A/B testing more accessible.

Data Science 66

Data Science Predictive Modeling Experimentation Deep Learning 66

Domino Data Lab

OCTOBER 7, 2020

MANOVA, for example, can test if the heights and weights in boys and girls is different. This statistical test is correct because the data are (presumably) bivariate normal. Statistics developed in the last century are based on probability models (distributions). The accuracy of any predictive model approaches 100%.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

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 83

Cost-Benefit Insurance Unstructured Data Machine Learning 83

IBM Big Data Hub

OCTOBER 16, 2023

Reinforcement learning uses ML to train models to identify and respond to cyberattacks and detect intrusions. Machine learning in financial transactions ML and deep learning are widely used in banking, for example, in fraud detection. Spotify uses ML models to generate its song recommendations.

Machine Learning 49

Machine Learning Marketing Forecasting Modeling 49

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

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

DataRobot Blog

DECEMBER 15, 2022

AI-powered Time Series Forecasting may be the most powerful aspect of machine learning available today. Working from datasets you already have, a Time Series Forecasting model can help you better understand seasonality and cyclical behavior and make future-facing decisions, such as reducing inventory or staff planning.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

Alation

APRIL 14, 2022

And, as organizations progress and grow, “data drift” starts to impact data usage, models, and your business. Automatic sampling to test transformation. They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning. Model Drift. Scheduling.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

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. 2f%% of the test set."

Statistics 69

Statistics Machine Learning Modeling Metrics 69

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

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

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

O'Reilly on Data

MARCH 24, 2020

In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. On the machine learning side, we are entering what Andrei Karpathy, director of AI at Tesla, dubs the Software 2.0

Machine Learning 343

Machine Learning Statistics Data Quality Modeling 343