Data mining, Deep Learning and Optimization

Data mining

Deep Learning

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 in business Predictive analytics draws its power from a wide range of methods and technologies, including big data, data mining, statistical modeling, machine learning, and assorted mathematical processes. Optimize raw material deliveries based on projected future demands. from 2022 to 2028.

Predictive Analytics

Predictive Analytics Analytics Predictive Modeling Forecasting

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Webinars

The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Communication

Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Manufacturing Sustainability Surge: Your Guide to Data-Driven Energy Optimization & Decarbonization

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

Understanding User Needs and Satisfying Them

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Trending Sources

12 data science certifications that will pay off

CIO Business Intelligence

JANUARY 19, 2024

The certification consists of several exams that cover topics such as machine learning, natural language processing, computer vision, and model forecasting and optimization. You should also have experience with pattern detection, experimentation in business optimization techniques, and time-series forecasting.

Data Science

Data Science Machine Learning Predictive Modeling Experimentation

Webinars

The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Communication

Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Manufacturing Sustainability Surge: Your Guide to Data-Driven Energy Optimization & Decarbonization

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

Understanding User Needs and Satisfying Them

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. Source ]. The Bottom Line.

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

Increasing Real-Time Efficiency Through AIOps

CIO Business Intelligence

APRIL 6, 2022

For example, on the front end, healthcare organizations can optimize secure access to clinical data to improve the level of care provided and reduce patient wait times. To create a productive, cost-effective analytics strategy that gets results, you need high performance hardware that’s optimized to work with the software you use.

Data mining

Data mining Deep Learning Insurance Digital Transformation

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

IBM Big Data Hub

JULY 6, 2023

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining. Machine learning and deep learning are both subsets of AI.

Machine Learning

Machine Learning Data Science Statistics Deep Learning

Data Science Journey Walkthrough – From Beginner to Expert

Smart Data Collective

JUNE 4, 2021

Here are the chronological steps for the data science journey. First of all, it is important to understand what data science is and is not. Data science should not be used synonymously with data mining. Mathematics, statistics, and programming are pillars of data science. Deep Learning.

Data Science

Data Science Statistics Deep Learning Machine Learning

Choosing the right Machine Learning Framework

Domino Data Lab

MARCH 11, 2021

In this article, we’ll show key considerations for selecting the right machine learning framework for your project and briefly review four popular ML frameworks. Here are several key considerations you should take into account when selecting a machine learning framework for your project. Parameter Optimization.

Machine Learning

Machine Learning Deep Learning Insurance Modeling

Meta-Learning For Better Machine Learning

Rocket-Powered Data Science

DECEMBER 16, 2018

An example of a cold start problem is k -Means Clustering, where the number of clusters k in the data set is not known in advance, and the locations of those clusters in feature space ( i.e., the cluster means) are not known either. What is missing in the above discussion is the deeper set of unknowns in the learning process.

Machine Learning

Machine Learning Metrics Optimization Data Science

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

Data mining

Data mining Machine Learning Deep Learning Marketing

Data Scientist’s Dilemma – The Cold Start Problem

Rocket-Powered Data Science

DECEMBER 15, 2018

If we cannot know that ( i.e., because it truly is unsupervised learning), then we would like to know at least that our final model is optimal (in some way) in explaining the data. This challenge is known as the cold-start problem ! In those intermediate steps it serves as an evaluation (or validation) metric.

Machine Learning

Machine Learning Optimization Metrics Modeling

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

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

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. Guestrin, C.,

Modeling

Modeling Deep Learning Machine Learning Knowledge Discovery

Using Big Data To Create An Award Winning Giveaway Bot

Smart Data Collective

APRIL 3, 2020

How Data-Driven Bots Can Help You. A couple of months ago, Hacker Moon wrote a great article on the use of deep learning to create chatbot s. This is one of the most important benefits of big data. Fortunately, big data is simplifying the research process as well. Chatbots for Giveaways.

Big Data

Big Data Data-driven Data mining Software

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

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

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

Increasing Real-Time Efficiency Through AIOps

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

Data Science Journey Walkthrough – From Beginner to Expert

Choosing the right Machine Learning Framework

Meta-Learning For Better Machine Learning

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

Data Scientist’s Dilemma – The Cold Start Problem

MLOps and the evolution of data science

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

Using Big Data To Create An Award Winning Giveaway Bot

How Do Super Rookies Start Learning Data Analysis?

How To Get An Award Winning Giveaway Bot

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

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