Flipboard

JULY 1, 2025

By Abid Ali Awan , KDnuggets Assistant Editor on July 1, 2025 in Data Science Image by Author | Canva Awesome lists are some of the most popular repositories on GitHub, often attracting thousands of stars from the community. It is ideal for data science projects, machine learning experiments, and anyone who wants to work with real-world data.

Data Science 169

Data Science Natural Language Processing Machine Learning Machine Learning 169

Dataconomy

MARCH 17, 2025

Overfitting in machine learning is a common challenge that can significantly impact a model’s performance. It occurs when a model becomes too tailored to the training data, resulting in its inability to generalize effectively to new, unseen datasets. What is overfitting in machine learning?

Machine Learning 103

Machine Learning Machine Learning Cross Validation Deep Learning 103

KDnuggets

JULY 8, 2025

During this phase, the pipeline identifies and pulls relevant data while maintaining connections to disparate systems that may operate on different schedules and formats. Next the transform phase represents the core processing stage, where extracted data undergoes cleaning, validation, and restructuring.

ETL 242

ETL Data Science Python Natural Language Processing 242

KDnuggets

JULY 15, 2025

Its key goals are to ensure data quality, consistency, and usability and align data with analytical models or reporting needs. This involves cleaning, standardizing, merging datasets, and applying business logic.

Data Pipeline 271

Data Pipeline Natural Language Processing Data Science SQL 271

KDnuggets

JUNE 19, 2025

Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Go vs. Python for Modern Data Workflows: Need Help Deciding?

Python 284

Python Natural Language Processing Data Science Machine Learning 284

Flipboard

MAY 5, 2025

When it comes to dealing with data quality, teams and companies fall into one of three modes: unmanaged, organized cleanup, or proactive

Data Quality 129

Data Quality Computer Science Computer Science Machine Learning 129

Flipboard

JULY 16, 2025

By Jayita Gulati on July 16, 2025 in Machine Learning Image by Editor In data science and machine learning, raw data is rarely suitable for direct consumption by algorithms. Data audit : Identify variable types (e.g., sum, difference, ratio, product) on existing variables.

Machine Learning 149

Machine Learning Machine Learning Natural Language Processing Data Science 149

KDnuggets

JULY 2, 2025

Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 5 Fun Python Projects for Absolute Beginners Bored of theory?

Python 299

Python Natural Language Processing Data Science Machine Learning 299

Flipboard

JUNE 12, 2025

When you understand distributions, you can spot data quality issues instantly. What youll learn: Start with descriptive statistics. You can start with clean data from sources like seaborns built-in datasets, then graduate to messier real-world data. Why its essential: Your data is in matrices.

Data Science 162

Data Science Natural Language Processing Hypothesis Testing Machine Learning 162

Dataversity

JUNE 20, 2025

However, the rapid explosion of data in terms of volume, speed, and diversity has brought about significant challenges in keeping that data reliable and high-quality.

Data Quality 66

Data Quality ML ML Data Governance 66

Pickl AI

MARCH 24, 2025

Summary: Adaptive Machine Learning is a cutting-edge technology that allows systems to learn and adapt in real-time by processing new data continuously. This capability is particularly important in today’s fast-paced environments, where data changes rapidly and requires systems that can learn and adapt in real time.

Machine Learning 52

Machine Learning Machine Learning Algorithm Artificial Intelligence 52

Dataconomy

JUNE 10, 2025

Noisy data can create significant obstacles in the realms of data analysis and machine learning. Understanding the complexities of noisy data is essential for improving data quality and enhancing the outcomes of predictive algorithms. What is noisy data?

Data Quality 91

Data Quality Data Analysis Data Analysis Data Mining 91

Pickl AI

FEBRUARY 17, 2025

Summary: Machine Learning’s key features include automation, which reduces human involvement, and scalability, which handles massive data. It uses predictive modelling to forecast future events and adaptiveness to improve with new data, plus generalization to analyse fresh data. What is Machine Learning?

Machine Learning 52

Machine Learning Machine Learning Supervised Learning ML 52

Dataconomy

JUNE 30, 2025

Over time, the relevance of GIGO has evolved, finding application not just in computing but also in data science, machine learning, and even social sciences. As data became more integral to operations in various sectors, understanding GIGO has become increasingly essential.

Data Quality 91

Data Quality Machine Learning Machine Learning Cross Validation 91

Flipboard

JULY 4, 2025

By Iván Palomares Carrascosa , KDnuggets Technical Content Specialist on July 4, 2025 in Python Image by Author | Ideogram Principal component analysis (PCA) is one of the most popular techniques for reducing the dimensionality of high-dimensional data. He trains and guides others in harnessing AI in the real world.

Python 132

Python Natural Language Processing Machine Learning Machine Learning 132

Dataconomy

NOVEMBER 11, 2024

Data preparation for LLM fine-tuning Proper data preparation is key to achieving high-quality results when fine-tuning LLMs for specific purposes. Importance of quality data in fine-tuning Data quality is paramount in the fine-tuning process.

Data Preparation 203

Data Preparation Database Data Quality Machine Learning 203

Dataconomy

JULY 7, 2025

These enhancements allow for faster querying and analysis, often utilizing machine learning (ML) algorithms and visualization tools. Usage by organizations Organizations across various sectors leverage data lakes to enhance their data capabilities.

Data Lakes 91

Data Lakes Data Warehouse Hadoop Analytics 91

AWS Machine Learning Blog

NOVEMBER 26, 2024

Neuron is the SDK used to run deep learning workloads on Trainium and Inferentia based instances. This data makes sure models are being trained smoothly and reliably. If failures increase, it may signal issues with data quality, model configurations, or resource limitations that need to be addressed.

AWS 108

AWS ML ML Data Pipeline 108

Dataconomy

JUNE 10, 2025

Data mining Data mining techniques identify trends and patterns in vast data collections, helping organizations uncover hidden opportunities. Retail analytics In retail, analytics forecast consumer behavior, optimizing inventory and sales strategies based on data-driven insights.

Analytics 91

Analytics Analytics Big Data Analytics Big Data Analytics 91

Towards AI

NOVEMBER 6, 2024

This story explores CatBoost, a powerful machine-learning algorithm that handles both categorical and numerical data easily. CatBoost is a powerful, gradient-boosting algorithm designed to handle categorical data effectively. But what if we could predict a student’s engagement level before they begin?

Cross Validation 114

Cross Validation Decision Trees Algorithm Machine Learning 114

Dataconomy

JUNE 18, 2025

It combines the cost-effectiveness and flexibility of data lakes with the performance and reliability of data warehouses. This hybrid approach facilitates advanced analytics, machine learning, and business intelligence, streamlining data processing and insights generation.

Data Lakes 91

Data Lakes Data Warehouse Business Intelligence Business Intelligence 91

Dataconomy

JUNE 11, 2025

Role of data governance Data governance is crucial for fostering an environment where data usage is responsible and compliant with regulations. Governance policies establish standards for data quality, ensuring that analytics outcomes are reliable and actionable.

Data Governance 91

Data Governance Business Intelligence Business Intelligence Data Quality 91

Dataconomy

MARCH 17, 2025

Augmented analytics is revolutionizing how organizations interact with their data. By harnessing the power of machine learning (ML) and natural language processing (NLP), businesses can streamline their data analysis processes and make more informed decisions.

Augmented Analytics 91

Augmented Analytics Analytics Analytics Natural Language Processing 91

AWS Machine Learning Blog

NOVEMBER 7, 2024

SageMaker JumpStart is a machine learning (ML) hub that provides a wide range of publicly available and proprietary FMs from providers such as AI21 Labs, Cohere, Hugging Face, Meta, and Stability AI, which you can deploy to SageMaker endpoints in your own AWS account. It’s serverless so you don’t have to manage the infrastructure.

AWS 113

AWS AI AI Machine Learning 113

Dataconomy

MARCH 11, 2025

Data versioning is a fascinating concept that plays a crucial role in modern data management, especially in machine learning. As datasets evolve through various modifications, the ability to track changes ensures that data scientists can maintain accuracy and integrity in their projects. What is data versioning?

Machine Learning 91

Machine Learning Machine Learning Data Scientist Data Quality 91

Dataconomy

APRIL 4, 2025

This approach recognizes that even the most sophisticated models are only as good as the data they are trained on. As industries increasingly rely on AI for decision-making, understanding the significance of data quality becomes critical for success. What is data-centric AI? Reduces errors related to data inconsistencies.

AI 103

AI AI Data Quality Algorithm 103

Dataconomy

JUNE 19, 2025

Cost efficiency Organizations can achieve cost efficiency through the shift toward real-time data streaming. Data quality and governance Domain-specific ownership leads to enhanced data quality since those closest to the data are responsible for maintaining it.

Data Lakes 91

Data Lakes Data Silos Data Quality ML 91

Flipboard

JUNE 13, 2025

You’ll discover how skills like data handling and machine learning form the backbone of AI innovation, while communication and collaboration ensure your ideas make an impact beyond the technical realm. Key languages include: Python: Known for its simplicity and versatility, Python is the most widely used language in AI.

Artificial Intelligence 68

Artificial Intelligence Artificial Intelligence Exploratory Data Analysis Machine Learning 68

NYU Center for Data Science

MAY 14, 2025

The paper identifies three key considerations for evaluating AI-enabled decision support systems (AI-DSS): scope, data quality, and human-machine interaction. Rudners technical expertise in robustness and transparency of machine learning systems provides a foundation for the policy recommendations in the brief.

Machine Learning 55

Machine Learning Machine Learning AI AI 55

ODSC - Open Data Science

NOVEMBER 29, 2024

Artificial Intelligence (AI) stands at the forefront of transforming data governance strategies, offering innovative solutions that enhance data integrity and security. In this post, let’s understand the growing role of AI in data governance, making it more dynamic, efficient, and secure. You can connect with him on LinkedIn.

Data Governance 40

Data Governance Predictive Analytics AI AI 40

AWS Machine Learning Blog

DECEMBER 4, 2024

Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. About the Authors Isaac Cameron is Lead Solutions Architect at Tecton, guiding customers in designing and deploying real-time machine learning applications.

ML 101

ML ML AWS AI 101

Pickl AI

NOVEMBER 24, 2024

Their applications include dimensionality reduction, feature learning, noise reduction, and generative modelling. Autoencoders enhance performance in downstream tasks and provide robustness against overfitting, making them versatile tools in Machine Learning. They help improve data quality by filtering out noise.

Deep Learning 52

Deep Learning Deep Learning Natural Language Processing Supervised Learning 52

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. Siamak Nariman is a Senior Product Manager at AWS. Madhubalasri B.

AWS 105

AWS ML ML Machine Learning 105

Dataconomy

APRIL 29, 2025

Training-serving skew is a significant concern in the machine learning domain, affecting the reliability of models in practical applications. Understanding how discrepancies between training data and operational data can impact model performance is essential for developing robust systems. What is training-serving skew?

Machine Learning 91

Machine Learning Machine Learning Data Preparation Data Quality 91

Pickl AI

JANUARY 12, 2025

Define AI-driven Practices AI-driven practices are centred on processing data, identifying trends and patterns, making forecasts, and, most importantly, requiring minimum human intervention. Data forms the backbone of AI systems, feeding into the core input for machine learning algorithms to generate their predictions and insights.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence AI AI 52

Flipboard

JUNE 23, 2025

De-duplication has always been essential to maintaining the quality of WorldCat by enhancing cataloging efficiency and streamlining quality. At OCLC, we’ve invested resources into a hybrid approach, leveraging AI to process vast amounts of data while ensuring catalogers and OCLC experts remain at the center of decision-making.

AI 143

AI AI Machine Learning Machine Learning 143

Snorkel AI

DECEMBER 2, 2024

The SageMaker Jumpstart machine learning hub offers a suite of tools for building, training, and deploying machine learning models at scale. When combined with Snorkel Flow, it becomes a powerful enabler for enterprises seeking to harness the full potential of their proprietary data.

AWS 52

AWS Machine Learning Machine Learning Data Preparation 52