Data mining, Deep Learning, Modeling and Optimization

Data mining

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Modeling

Optimization

Rapidminer Platform Supports Entire Data Science Lifecycle

David Menninger's Analyst Perspectives

SEPTEMBER 16, 2021

Rapidminer is a visual enterprise data science platform that includes data extraction, data mining, deep learning, artificial intelligence and machine learning (AI/ML) and predictive analytics. Rapidminer Studio is its visual workflow designer for the creation of predictive models.

Data Science

Data Science Data Lake Data mining Deep Learning

What is predictive analytics? Transforming data into future insights

CIO Business Intelligence

FEBRUARY 10, 2023

Predictive analytics definition Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. Financial services: Develop credit risk models. from 2022 to 2028.

Predictive Analytics

Predictive Analytics Analytics Predictive Modeling Forecasting

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The Key to Sustainable Energy Optimization: A Data-Driven Approach for Manufacturing

From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Understanding User Needs and Satisfying Them

Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

How To Get Promoted In Product Management

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Trending Sources

12 data science certifications that will pay off

CIO Business Intelligence

JANUARY 19, 2024

The exam covers everything from fundamental to advanced data science concepts such as big data best practices, business strategies for data, building cross-organizational support, machine learning, natural language processing, scholastic modeling, and more.

Data Science

Data Science Machine Learning Predictive Modeling Experimentation

Webinars

The Key to Sustainable Energy Optimization: A Data-Driven Approach for Manufacturing

From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Understanding User Needs and Satisfying Them

Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

How To Get Promoted In Product Management

MORE WEBINARS

An Important Guide To Unsupervised Machine Learning

Smart Data Collective

NOVEMBER 1, 2020

With that being said, let’s have a closer look at how unsupervised machine learning is omnipresent in all industries. What Is Unsupervised Machine Learning? If you’ve ever come across deep learning, you might have heard about two methods to teach machines: supervised and unsupervised. Unsupervised ML: The Basics.

Machine Learning

Machine Learning Data mining Visualization Statistics

Predictive Analytics: 4 Primary Aspects of Predictive Analytics

Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

Predictive Analytics

Predictive Analytics Analytics Predictive Modeling Data mining

Meta-Learning For Better Machine Learning

Rocket-Powered Data Science

DECEMBER 16, 2018

In a related post we discussed the Cold Start Problem in Data Science — how do you start to build a model when you have either no training data or no clear choice of model parameters. There are also examples of cold start in supervised machine learning (where you do have class labels on the training data).

Machine Learning

Machine Learning Metrics Optimization Data Science

Choosing the right Machine Learning Framework

Domino Data Lab

MARCH 11, 2021

Machine learning (ML) frameworks are interfaces that allow data scientists and developers to build and deploy machine learning models faster and easier. Machine learning is used in almost every industry, notably finance , insurance , healthcare , and marketing. Parameter Optimization.

Machine Learning

Machine Learning Deep Learning Insurance Modeling

Data Scientist’s Dilemma – The Cold Start Problem

Rocket-Powered Data Science

DECEMBER 15, 2018

In these applications, there is no requirement or presumption regarding the existence of labeled training data — we are essentially parameterizing or characterizing the patterns in the data ( e.g., the trends, correlations, segments, clusters, associations). How do we know that our progression is moving towards more accurate models?

Machine Learning

Machine Learning Optimization Metrics Modeling

Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning

Machine Learning Data Science Statistics Deep Learning

Leveraging user-generated social media content with text-mining examples

IBM Big Data Hub

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. What is text mining? For instance, a parsing model could identify the subject, verb and object of a complete sentence. You may need to iterate this process until the results are satisfactory.

Data mining

Data mining Machine Learning Deep Learning Marketing

Data Science Journey Walkthrough – From Beginner to Expert

Smart Data Collective

JUNE 4, 2021

Since the field covers such a vast array of services, data scientists can find a ton of great opportunities in their field. Data scientists use algorithms for creating data models. These data models predict outcomes of new data. Data science is one of the highest-paid jobs of the 21st century.

Data Science

Data Science Statistics Deep Learning Machine Learning

MLOps and the evolution of data science

IBM Big Data Hub

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

Data Science

Data Science Machine Learning Cost-Benefit Deep Learning

How Do Super Rookies Start Learning Data Analysis?

FineReport

DECEMBER 19, 2019

Professional data analysts must have a wealth of business knowledge in order to know from the data what has happened and what is about to happen. In addition, tools for data analysis and data mining are also important. Excel, Python, Power BI, Tableau, FineReport are frequently used by data analysts.

Knowledge Discovery

Knowledge Discovery Visualization Experimentation Reporting

Using Big Data To Create An Award Winning Giveaway Bot

Smart Data Collective

APRIL 3, 2020

Communications that are 24/7 is not possible with traditional business models. Fortunately, new advances in data science have made it easier than ever to reply to customers any time of the day. How Data-Driven Bots Can Help You. This is one of the most important benefits of big data. Chatbots for Giveaways.

Big Data

Big Data Data-driven Data mining Software

How To Get An Award Winning Giveaway Bot

Smart Data Collective

APRIL 3, 2020

Big Data

Big Data Data-driven Data mining Software

Incorporating Artificial Intelligence for Businesses : The Modern Approach to Data Analytics

BizAcuity

MARCH 31, 2022

Marketers also have access to several AI softwares to save time and optimize their work at every step of the funnel. Integrating IoT and route optimization are two other important places that use AI. A lot of testing AI methods can be utilized for better and more accurate outcomes from mining the data. AI in Healthcare.

Data Analytics

Data Analytics Analytics Prescriptive Analytics Predictive Analytics

Explaining black-box models using attribute importance, PDPs, and LIME

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

Modeling Deep Learning Machine Learning Knowledge Discovery

Data Leaders Brief

Rapidminer Platform Supports Entire Data Science Lifecycle

What is predictive analytics? Transforming data into future insights

Webinars

Trending Sources

12 data science certifications that will pay off

Webinars

An Important Guide To Unsupervised Machine Learning

Predictive Analytics: 4 Primary Aspects of Predictive Analytics

Meta-Learning For Better Machine Learning

Choosing the right Machine Learning Framework

Data Scientist’s Dilemma – The Cold Start Problem

Data science vs. machine learning: What’s the difference?

Leveraging user-generated social media content with text-mining examples

Data Science Journey Walkthrough – From Beginner to Expert

MLOps and the evolution of data science

How Do Super Rookies Start Learning Data Analysis?

Using Big Data To Create An Award Winning Giveaway Bot

How To Get An Award Winning Giveaway Bot

Incorporating Artificial Intelligence for Businesses : The Modern Approach to Data Analytics

Explaining black-box models using attribute importance, PDPs, and LIME

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