Dataconomy

JULY 24, 2023

If you’ve found yourself asking, “How to become a data scientist?” In this detailed guide, we’re going to navigate the exciting realm of data science, a field that blends statistics, technology, and strategic thinking into a powerhouse of innovation and insights. What is a data scientist?

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Data Science Dojo

APRIL 3, 2023

Drag and drop tools have revolutionized the way we approach machine learning (ML) workflows. Gone are the days of manually coding every step of the process – now, with drag-and-drop interfaces, streamlining your ML pipeline has become more accessible and efficient than ever before. This is where drag-and-drop tools come in.

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Data Science Dojo

SEPTEMBER 23, 2024

This powerful yet simple concept helps data scientists and machine learning practitioners assess the accuracy of classification algorithms , providing insights into how well a model is performing in predicting various classes. One of the most fundamental tools for this purpose is the confusion matrix.

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Machine Learning Machine Learning Data Scientist ML 434

Data Science Dojo

NOVEMBER 10, 2023

Data science has become an increasingly important field in recent years, as the amount of data generated by businesses, organizations, and individuals has grown exponentially. Uses of generative AI for data scientists Generative AI can help data scientists with their projects in a number of ways.

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Data Science Data Scientist Natural Language Processing ML 332

Data Science Dojo

FEBRUARY 13, 2025

If you want to stay ahead in the world of big data, AI, and data-driven decision-making, Big Data & AI World 2025 is the perfect event to explore the latest innovations, strategies, and real-world applications. Thats where Data + AI Summit 2025 comes in!

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Data Science Dojo

MARCH 31, 2023

Artificial intelligence (AI), machine learning (ML), and data science have become some of the most significant topics of discussion in today’s technological era. Matul, who has experience working as an AI scientist at amazon, focused on dialogue machines and natural language understanding.

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Data Science Dojo

JULY 5, 2024

Machine learning models are algorithms designed to identify patterns and make predictions or decisions based on data. These models are trained using historical data to recognize underlying patterns and relationships. Once trained, they can be used to make predictions on new, unseen data.

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KDnuggets

MARCH 25, 2020

Read our analysis of coronavirus data and poll results; Use your time indoors to learn with 24 best and free books to understand Machine Learning; Study the 9 important lessons from the first year as a Data Scientist; Understand the SVM, a top ML algorithm; check a comprehensive list of AI resources for online learning; and more.

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Dataconomy

MAY 13, 2020

Data scientists are constantly challenged with improving their ML models. But when a new algorithm won’t improve your AUC there’s only one place to look: DATA. This guide walks you through six easy steps for data acquisition, a complete checklist for data provider due diligence, and data provider tests to.

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Dataconomy

MARCH 27, 2023

However, while RPA and ML share some similarities, they differ in functionality, purpose, and the level of human intervention required. In this article, we will explore the similarities and differences between RPA and ML and examine their potential use cases in various industries. What is machine learning (ML)?

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Flipboard

JANUARY 6, 2025

The agency wanted to use AI [artificial intelligence] and ML to automate document digitization, and it also needed help understanding each document it digitizes, says Duan. The demand for modernization is growing, and Precise can help government agencies adopt AI/ML technologies.

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IBM Data Science in Practice

MARCH 13, 2023

Co-authored by Carolyn Saplicki , Data Scientist with Expert Labs, and Mitali Bante , Data Scientist with Expert Labs. This blog focuses on pre-processing algorithms. Pre-processing algorithms involve modifying the dataset before training the model to remove or reduce the bias present in the data.

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Machine Learning Machine Learning Algorithm Data Scientist 130

AWS Machine Learning Blog

OCTOBER 24, 2024

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. Let’s learn about the services we will use to make this happen.

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AWS Machine Learning Blog

MAY 9, 2024

The rapid advancements in artificial intelligence and machine learning (AI/ML) have made these technologies a transformative force across industries. An effective approach that addresses a wide range of observed issues is the establishment of an AI/ML center of excellence (CoE). What is an AI/ML CoE?

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Dataconomy

FEBRUARY 19, 2020

This free guide walks you through six easy steps for data acquisition, a complete checklist for data provider due diligence, and data provider tests to uplift your model’s accuracy. Data scientists are constantly challenged with improving their ML models.

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Machine Learning Machine Learning Data Scientist ML 31

Smart Data Collective

AUGUST 29, 2022

They are digging deeper into their data to improve efficiency, gain a competitive advantage, and further increase their profit. This is why businesses are looking to leverage machine learning (ML). In this article, we will share some best practices for improving your analytics with ML. Times are changing — for the better!

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Dataconomy

JULY 30, 2019

A recent survey of over 225 enterprise Data Scientists, AI technologists and business stakeholders involved in active AI and machine learning (ML) projects, suggests that for most organizations, it’s still early days for AI technology. The post Why 96% of Enterprises Face AI Training Data Issues appeared first on Dataconomy.

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Data Science Dojo

FEBRUARY 20, 2023

Machine learning (ML) is the technology that automates tasks and provides insights. It allows data scientists to build models that can automate specific tasks. It comes in many forms, with a range of tools and platforms designed to make working with ML more efficient. It also has ML algorithms built into the platform.

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Dataconomy

MAY 2, 2023

They design, develop, and deploy the machine learning algorithms that power everything from self-driving cars to personalized recommendations. In the context of a business, machine learning engineers are responsible for creating bots that are utilized for chat purposes or data collection. Is ML engineering a stressful job?

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Data Science Dojo

JANUARY 22, 2025

To get you started, Data Science Dojo and Weaviate have teamed up to bring you an exciting webinar series: Master Vector Embeddings with Weaviate. We have carefully curated the series to empower AI enthusiasts, data scientists, and industry professionals with a deep understanding of vector embeddings.

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IBM Data Science in Practice

JANUARY 9, 2024

By Carolyn Saplicki , IBM Data Scientist Industries are constantly seeking innovative solutions to maximize efficiency, minimize downtime, and reduce costs. Many businesses are in different stages of their MAS AI/ML modernization journey. All data scientists could leverage our patterns during an engagement.

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Flipboard

NOVEMBER 30, 2023

Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. Deploy traditional models to SageMaker endpoints In the following examples, we showcase how to use ModelBuilder to deploy traditional ML models.

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Data Science Dojo

SEPTEMBER 5, 2024

It encompasses both theoretical and practical topics, including data structures, algorithms, hardware, and software. Key Areas of Study Key areas of study within computer science include: Algorithms : Procedures or formulas for solving problems. Data Structures : Ways to organize, manage, and store data efficiently.

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Data Science Dojo

SEPTEMBER 5, 2024

It encompasses both theoretical and practical topics, including data structures, algorithms, hardware, and software. Key Areas of Study Key areas of study within computer science include: Algorithms : Procedures or formulas for solving problems. Data Structures : Ways to organize, manage, and store data efficiently.

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Computer Science Computer Science Data Science Machine Learning 195

AWS Machine Learning Blog

FEBRUARY 12, 2024

The Ranking team at Booking.com plays a pivotal role in ensuring that the search and recommendation algorithms are optimized to deliver the best results for their users. Essential ML capabilities such as hyperparameter tuning and model explainability were lacking on premises.

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Towards AI

NOVEMBER 8, 2024

We don’t have better algorithms; we just have more data. Peter Norvig, The Unreasonable Effectiveness of Data. Edited Photo by Taylor Vick on Unsplash In ML engineering, data quality isn’t just critical — it’s foundational. Because of how ML practitioners were initially trained.

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Towards AI

JULY 1, 2024

Learn the basics of data engineering to improve your ML modelsPhoto by Mike Benna on Unsplash It is not news that developing Machine Learning algorithms requires data, often a lot of data. Collecting this data is not trivial, in fact, it is one of the most relevant and difficult parts of the entire workflow.

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AWS Machine Learning Blog

JANUARY 31, 2025

Increasingly, FMs are completing tasks that were previously solved by supervised learning, which is a subset of machine learning (ML) that involves training algorithms using a labeled dataset. About the Authors Jordan Knight is a Senior Data Scientist working for Travelers in the Business Insurance Analytics & Research Department.

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IBM Journey to AI blog

MAY 20, 2024

Machine learning (ML) has become a critical component of many organizations’ digital transformation strategy. From predicting customer behavior to optimizing business processes, ML algorithms are increasingly being used to make decisions that impact business outcomes.

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Google Research AI blog

MARCH 30, 2023

Posted by Peter Mattson, Senior Staff Engineer, ML Performance, and Praveen Paritosh, Senior Research Scientist, Google Research, Brain Team Machine learning (ML) offers tremendous potential, from diagnosing cancer to engineering safe self-driving cars to amplifying human productivity. Each step can introduce issues and biases.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.

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ODSC - Open Data Science

MARCH 22, 2023

Be sure to check out his session, “ Improving ML Datasets with Cleanlab, a Standard Framework for Data-Centric AI ,” there! Anybody who has worked on a real-world ML project knows how messy data can be. Our goal is to enable all developers to find and fix data issues as effectively as today’s best data scientists.

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AWS Machine Learning Blog

APRIL 26, 2023

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Challenges Customers may face several challenges when implementing machine learning (ML) solutions.

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DrivenData Labs

APRIL 8, 2025

These applications are all enabled by a strong ecosystem of open-source Python packages for working with image data. Packages like rasterio and pydicom make it possible for data scientists to contribute without becoming experts in satellites or medical imagery. Overall, we recommend openSMILE for general ML applications.

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Towards AI

MAY 3, 2023

In the realm of data science, seasoned professionals often carry out research to comprehend how similar issues have been tackled in the past. They investigate the most suitable algorithms, identify the best weights and hyperparameters, and might even collaborate with fellow data scientists in the community to develop an effective strategy.

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AWS Machine Learning Blog

JULY 6, 2023

Many organizations choose SageMaker as their ML platform because it provides a common set of tools for developers and data scientists. This is usually in a dedicated customer AWS account, meaning there still needs to be cross-account access to the customer AWS account where SageMaker is running.

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O'Reilly Media

JANUARY 17, 2023

This mindset has followed me into my work in ML/AI. Because if companies use code to automate business rules, they use ML/AI to automate decisions. Given that, what would you say is the job of a data scientist (or ML engineer, or any other such title)? But first, let’s talk about the typical ML workflow.

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