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

IBM Big Data Hub

JULY 6, 2023

Python is the most common programming language used in machine learning. Machine learning and deep learning are both subsets of AI. Deep learning teaches computers to process data the way the human brain does. Deep learning algorithms are neural networks modeled after the human brain.

Machine Learning 57

Machine Learning Data Science Statistics Deep Learning 57

IBM Big Data Hub

FEBRUARY 14, 2024

By infusing AI into IT operations , companies can harness the considerable power of NLP, big data, and ML models to automate and streamline operational workflows, and monitor event correlation and causality determination. 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

bridgei2i

DECEMBER 15, 2021

Imperative to predicting user preferences or interests and suggestions, the recommendation engine market size is projected to reach $12.03 This is especially useful in the right product placements over different channels based on user interests and preferences, and different time slots, optimizing the chance of conversions.

Recreation/Entertainment 52

Recreation/Entertainment Metadata Advertising Predictive Modeling 52

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. A targeted approach will optimize the user experience and enhance an organization’s ROI.

Data mining 56

Data mining Machine Learning Deep Learning Marketing 56

O'Reilly on Data

JUNE 18, 2019

Even if we boosted the quality of the available data via unification and cleaning, it still might not be enough to power the even more complex analytics and predictions models (often built as a deep learning model). Data programming.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

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

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). Computer vision fuels self-driving cars.

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. Source: mathworks.com.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

Cloudera

FEBRUARY 16, 2022

By logging the performance of every combination of search parameters within an experiment, we can choose the optimal set of parameters when building a model. 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.

Machine Learning 82

Machine Learning Modeling Metadata Recreation/Entertainment 82

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. Now that the class imbalance has been resolved, we can move forward with the actual model training. Model training.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

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

ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions. In other words, ML leverages input data to predict outputs, continuously updating outputs as new data becomes available.

Machine Learning 79

Machine Learning Modeling Deep Learning Predictive Modeling 79

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 100

Big Data Cost-Benefit IoT Machine Learning 100

CIO Business Intelligence

MARCH 22, 2023

The new class often uses advanced techniques such as deep learning, natural language processing, and computer vision to analyze and extract insights from the data. Homomorphic encryption often requires custom hardware to optimize performance, which can be expensive to develop and maintain.

Machine Learning 99

Machine Learning Cost-Benefit Modeling Risk 99

Alation

APRIL 14, 2022

They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning. Governance influences how an organization’s objectives are set and achieved, how risk is monitored and addressed, and how performance is optimized.” It’s not a simple definition.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

O'Reilly on Data

SEPTEMBER 11, 2019

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. But the database—or, more precisely, the data model —is no longer the sole or, arguably, the primary focus of data engineering. 40; it peaked at Strata NY 2018 at No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20