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 73

Machine Learning Modeling Deep Learning Predictive Modeling 73

Cloudera

FEBRUARY 16, 2022

The greater our understanding of how a model works, the better we are able to predict what the output will be for a range of inputs or changes to the model’s parameters. Given the complexity of some ML models, especially those based on Deep Learning (DL) Convolutional Neural Networks (CNNs), there are limits to interpretability.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

IBM Big Data Hub

JULY 6, 2023

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics. Python is the most common programming language used in machine learning.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

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

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. If necessary, make adjustments to the preprocessing, representation and/or modeling steps to improve the results.

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

IBM Big Data Hub

OCTOBER 16, 2023

ML also provides the ability to closely monitor a campaign by checking open and clickthrough rates, among other metrics. Machine learning in financial transactions ML and deep learning are widely used in banking, for example, in fraud detection. Then, it can tailor marketing materials to match those interests.

Machine Learning 48

Machine Learning Marketing Forecasting Modeling 48

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.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

CIO Business Intelligence

MAY 6, 2025

Predictive analytics: Turning insight into foresight Predictive analytics uses historical data and statistical models or machine learning algorithms to answer the question, What is likely to happen? Measure key metrics before model deployment, then track improvements over time.

Prescriptive Analytics 78

Prescriptive Analytics Predictive Analytics Descriptive Analytics ROI 78