O'Reilly on Data

DECEMBER 12, 2019

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Analytics Vidhya

JULY 5, 2022

This article was published as a part of the Data Science Blogathon. Introduction to Imbalanced Datasets The accuracy achieved by many of the machine learning models using traditional statistical algorithms increases by just around 2% or so when the size of the training dataset is increased from 20% to 80%.

Statistics 331

Statistics Machine Learning Data Science Publishing 331

O'Reilly on Data

NOVEMBER 13, 2018

Considerations for a world where ML models are becoming mission critical. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. Interest on the part of companies means the demand side for “machine learning talent” is healthy.

Machine Learning 220

Machine Learning Risk Management Statistics 220

Analytics Vidhya

MARCH 1, 2022

This article was published as a part of the Data Science Blogathon. Credit evaluations have progressed from being subjective decisions by the bank’s credit experts to a more statistically advanced evaluation. The post Gaussian Naive Bayes Algorithm for Credit Risk Modelling appeared first on Analytics Vidhya.

Risk 258

Risk Modeling Statistics Data Science 258

Analytics Vidhya

JUNE 16, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Using the machine learning models in your projects is quite. The post Getting Started with Machine Learning?—?Implementing Implementing Linear Regression from Scratch appeared first on Analytics Vidhya.

Machine Learning 143

Machine Learning Data Science Publishing Modeling 143

Analytics Vidhya

JANUARY 20, 2022

This article was published as a part of the Data Science Blogathon. The post A brief introduction to Multilevel Modelling appeared first on Analytics Vidhya.

Modeling 314

Modeling Testing Data Science Publishing 314

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is machine learning? This post will dive deeper into the nuances of each field.

Machine Learning 59

Machine Learning Data Science Statistics Deep Learning 59

Analytics Vidhya

MAY 4, 2022

This article was published as a part of the Data Science Blogathon. Introduction to Random Forest Missing values have always been a concern for any statistical analysis. They significantly reduce the study’s statistical powers, which may lead to faulty conclusions.

Statistics 387

Statistics Data Science Publishing Modeling 387

Smart Data Collective

OCTOBER 8, 2021

Learn how genetic algorithms and machine learning can help hedge fund organizations manage a business. This article looks at how genetic algorithms (GA) and machine learning (ML) can help hedge fund organizations. Modern machine learning and back-testing; how quant hedge funds use it.

Machine Learning 115

Machine Learning Testing Strategy Modeling 115

Smart Data Collective

NOVEMBER 1, 2020

Machines, artificial intelligence (AI), and unsupervised learning are reshaping the way businesses vie for a place under the sun. 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? Source ].

Machine Learning 134

Machine Learning Data mining Visualization Statistics 134

Analytics Vidhya

JULY 9, 2022

This article was published as a part of the Data Science Blogathon. Introduction In order to build machine learning models that are highly generalizable to a wide range of test conditions, training models with high-quality data is essential.

Machine Learning 362

Machine Learning Data Science Data Collection Testing 362

Analytics Vidhya

OCTOBER 28, 2020

This article was published as a part of the Data Science Blogathon. Introduction A popular and widely used statistical method for time series forecasting. The post How to Create an ARIMA Model for Time Series Forecasting in Python appeared first on Analytics Vidhya.

Forecasting 197

Forecasting Modeling Statistics Data Science 197

Analytics Vidhya

OCTOBER 11, 2020

This article was published as a part of the Data Science Blogathon. So you have successfully built your classification model. The post HOW TO CHOOSE EVALUATION METRICS FOR CLASSIFICATION MODEL appeared first on Analytics Vidhya. INTRODUCTION Yay!! What should.

Metrics 315

Metrics Modeling Data Science Publishing 315

Analytics Vidhya

JANUARY 4, 2023

This article was published as a part of the Data Science Blogathon. Introduction Machine learning algorithms are one of the essential parameters while training and building an intelligent model for some of the problem statements. The Naive Bayes […].

Machine Learning 260

Machine Learning Data Science Publishing Modeling 260

Analytics Vidhya

DECEMBER 16, 2020

This article was published as a part of the Data Science Blogathon. The post Decluttering the performance measures of classification models appeared first on Analytics Vidhya. Introduction There are so many performance evaluation measures when it comes to.

Measurement 290

Measurement Modeling Data Science Publishing 290

Analytics Vidhya

JULY 23, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Logistic Regression is another statistical model which is used for. The post Geometrical Approach To Understand Logistic Regression appeared first on Analytics Vidhya.

Statistics 337

Statistics Data Science Publishing Modeling 337

Analytics Vidhya

FEBRUARY 17, 2022

This article was published as a part of the Data Science Blogathon. Introduction Machine learning models are garbage in garbage-out boxes, and it is essential to address any missing data before feeding it to your model. 2) The […].

Machine Learning 280

Machine Learning Data Science Publishing Modeling 280

Analytics Vidhya

MAY 28, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Logistic Regression, a statistical model is a very popular and. The post 20+ Questions to Test your Skills on Logistic Regression appeared first on Analytics Vidhya.

Testing 314

Testing Statistics Data Science Publishing 314

O'Reilly on Data

MARCH 26, 2024

TL;DR LLMs and other GenAI models can reproduce significant chunks of training data. Researchers are finding more and more ways to extract training data from ChatGPT and other models. And the space is moving quickly: SORA , OpenAI’s text-to-video model, is yet to be released and has already taken the world by storm.

Modeling 275

Modeling Machine Learning Risk Advertising 275

Analytics Vidhya

MARCH 29, 2022

This article was published as a part of the Data Science Blogathon. Introduction to Linear Regression Image 1: Sales vs Budget data with a linear model representation Linear regression is a statistical method that presumes a linear relationship between the input and the output variables.

Statistics 253

Statistics Sales Data Science Publishing 253

Analytics Vidhya

NOVEMBER 25, 2020

This article was published as a part of the Data Science Blogathon. The post Creating Linear Model, It’s Equation and Visualization for Analysis appeared first on Analytics Vidhya. Introduction Have you ever been tasked with visualizing the relationship between each.

Visualization 281

Visualization Modeling Data Science Publishing 281

Analytics Vidhya

MAY 22, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction In Machine learning or Deep Learning, some of the models. The post How to transform features into Normal/Gaussian Distribution appeared first on Analytics Vidhya.

Deep Learning 295

Deep Learning Machine Learning Data Science Publishing 295

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI As model building become easier, the problem of high-quality data becomes more evident than ever. In this post, we shed some light on various efforts toward generating data for machine learning (ML) models.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

Analytics Vidhya

SEPTEMBER 28, 2021

This article was published as a part of the Data Science Blogathon In this tutorial, we will learn: Introduction to Natural Language Processing Phases of Natural Language Processing Introduction to spaCy Installation and local setup of spaCy Statistical models available in spaCy Reading and processing text Spans Tokenization Sentence Detection Stopwords (..)

Statistics 257

Statistics Data Science Publishing Modeling 257

Rocket-Powered Data Science

MARCH 19, 2021

One CIO said it this way , “If CIOs invested in machine learning three years ago, they would have wasted their money. This article quotes an older market projection (from 2019) , which estimated “the global industrial IoT market could reach $14.2 But if they wait another three years, they will never catch up.”

Internet of Things 259

Internet of Things Analytics IoT Prescriptive Analytics 259

Smart Data Collective

JUNE 4, 2021

Data scientists use algorithms for creating data models. These data models predict outcomes of new data. Data science needs knowledge from a variety of fields including statistics, mathematics, programming, and transforming data. Mathematics, statistics, and programming are pillars of data science. Statistics.

Data Science 105

Data Science Statistics Deep Learning Machine Learning 105

CIO Business Intelligence

AUGUST 11, 2023

How natural language processing works NLP leverages machine learning (ML) algorithms trained on unstructured data, typically text, to analyze how elements of human language are structured together to impart meaning. Transformer models take applications such as language translation and chatbots to a new level.

Unstructured Data 138

Unstructured Data Modeling Machine Learning Data mining 138

Smart Data Collective

DECEMBER 19, 2021

The rise of machine learning and the use of Artificial Intelligence gradually increases the requirement of data processing. That’s because the machine learning projects go through and process a lot of data, and that data should come in the specified format to make it easier for the AI to catch and process.

Optimization 135

Optimization Machine Learning Data mining Statistics 135

Rocket-Powered Data Science

JULY 7, 2019

AGI (Artificial General Intelligence): AI (Artificial Intelligence): Application of Machine Learning algorithms to robotics and machines (including bots), focused on taking actions based on sensory inputs (data). Examples: (1-3) All those applications shown in the definition of Machine Learning. (4) See [link].

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

Rocket-Powered Data Science

MARCH 10, 2020

And this: perhaps the most powerful node in a graph model for real-world use cases might be “context”. How does one express “context” in a data model? Ahh, that’s the topic for another article. Hence, the graph model can be applied productively and effectively in numerous network analysis use cases.

Metadata 250

Metadata Machine Learning ROI Prescriptive Analytics 250

Smart Data Collective

NOVEMBER 18, 2020

The Bureau of Labor Statistics estimates that the number of data scientists will increase from 32,700 to 37,700 between 2019 and 2029. Previously, such problems were dealt with by specialists in mathematics and statistics. Statistics, mathematics, linear algebra. Machine learning. Where to Use Data Science?

Data mining 143

Data mining Data Science Informatics Statistics 143

Domino Data Lab

JULY 28, 2021

Through a marriage of traditional statistics with fast-paced, code-first computer science doctrine and business acumen, data science teams can solve problems with more accuracy and precision than ever before, especially when combined with soft skills in creativity and communication. Math and Statistics Expertise.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

Ontotext

DECEMBER 15, 2023

It includes only ML papers and related entities; this SPARQL query shows some statistics: papers tasks models datasets methods evaluations repos 376557 4267 24598 8322 2101 52519 153476 We can start with these repositories (most of them are on Github) and get all their topics. We use Categories as a way of finding relevant articles.

Machine Learning 69

Machine Learning Interactive Deep Learning Statistics 69

Analytics Vidhya

MARCH 22, 2022

This article was published as a part of the Data Science Blogathon. Introduction Feature analysis is an important step in building any predictive model. In this article, we will look into a very simple feature analysis technique that can be used in cases such as […].

Predictive Modeling 260

Predictive Modeling Data Science Publishing Modeling 260

DataRobot Blog

JULY 28, 2022

Rapid advances in machine learning in recent years have begun to lower the technical hurdles to implementing AI, and various companies have begun to actively use machine learning. Ultimately, the evaluation is based on whether or not the model delivers success to the customers’ business.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

Smart Data Collective

OCTOBER 7, 2023

The daily articles we are seeing about the changes brought on by ChatGPT are clear evidence of the changes it has created. A number of articles have been written about the profound impact that CyberLink has had on the people tracing software industry, as a result of using AI technology.

Technology 94

Technology Snapshot Data-driven Machine Learning 94

Analytics Vidhya

DECEMBER 4, 2020

This article was published as a part of the Data Science Blogathon. Introduction Random Forests are always referred to as black-box models. Let’s try. The post Lets Open the Black Box of Random Forests appeared first on Analytics Vidhya.

Data Science 366

Data Science Publishing Modeling Analytics 366