Rocket-Powered Data Science

JULY 6, 2021

Customer purchase patterns, supply chain, inventory, and logistics represent just a few domains where we see new and emergent behaviors, responses, and outcomes represented in our data and in our predictive models. 4) AIOps increasingly became a focus in AI strategy conversations. And the goodness doesn’t stop there.

Machine Learning 279

Machine Learning Data-driven IoT Deep Learning 279

Smarten

APRIL 19, 2024

Anomaly Alerts KPI monitoring and Auto Insights allows business users to quickly establish KPIs and target metrics and identify the Key Influencers and variables for the target KPI.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

IBM Big Data Hub

AUGUST 28, 2023

With nearly 5 billion users worldwide—more than 60% of the global population —social media platforms have become a vast source of data that businesses can leverage for improved customer satisfaction, better marketing strategies and faster overall business growth.

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

O'Reilly on Data

DECEMBER 12, 2019

More structured approaches to sensitivity analysis include: Adversarial example searches : this entails systematically searching for rows of data that evoke strange or striking responses from an ML model. Figure 1 illustrates an example adversarial search for an example credit default ML model. What can you do? Data augmentation.

Machine Learning 198

Machine Learning Modeling Testing Risk Management 198

Domino Data Lab

APRIL 21, 2021

deep learning) there is no guaranteed explainability. We will go through a typical ML pipeline, where we do data ingestion, exploratory data analysis, feature engineering, model training and evaluation. from sklearn import metrics. A drawback of the ML approach is that there for certain algorithms (e.g. 0.01, 0.001] }.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

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 74

Machine Learning Modeling Deep Learning Predictive Modeling 74