Dataconomy

APRIL 2, 2025

Understanding RMSE is crucial for data scientists, statisticians, and anyone involved in forecasting or regression analysis. Why is RMSE important in machine learning? In the realm of machine learning, RMSE serves a crucial role in assessing the effectiveness of predictive algorithms.

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Dataconomy

MARCH 20, 2025

Model behavior in machine learning is a multifaceted concept that encapsulates how predictive models make decisions based on the data they process. Understanding model behavior not only sharpens our grasp of machine learning systems but also illuminates the challenges and opportunities tied to predictive accuracy.

Machine Learning 91

Machine Learning Machine Learning Cross Validation Data Scientist 91

Data Science Dojo

AUGUST 24, 2023

While DevOps and MLOps share many similarities, MLOps requires a more specialized set of tools and practices to address the unique challenges posed by data-driven and computationally intensive ML workflows. Examples include: Cross-validation techniques for better model evaluation.

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Mlearning.ai

FEBRUARY 2, 2023

Data scientists use a technique called cross validation to help estimate the performance of a model as well as prevent the model from… Continue reading on MLearning.ai »

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

This guest post is co-written by Lydia Lihui Zhang, Business Development Specialist, and Mansi Shah, Software Engineer/Data Scientist, at Planet Labs. In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image.

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Dataconomy

MARCH 10, 2025

This exploration delves into the essential aspects of ML model parameters and associated concepts, revealing their role in effective machine learning. ML model parameters are the underlying variables adjusted during training to fit the model to the data. They ensure that the model learns effectively and generalizes well.

ML 91

ML ML Cross Validation Machine Learning 91

Towards AI

FEBRUARY 3, 2025

Photo by Agence Olloweb on Unsplash Machine learning model selection has always been a challenge. Whether youre predicting stock prices, diagnosing diseases, or optimizing marketing campaigns, the question remains: which model works best for my data? Upgrade to access all of Medium.

Supervised Learning 53

Supervised Learning Cross Validation Data Scientist Machine Learning 53

DataRobot Blog

JULY 5, 2022

Location data is a key dimension whose volume and availability has grown exponentially in the last decade. A Light Gradient Boosted Trees Regressor with Early Stopping model was trained without any geospatial data on 5,657 residential home listings to provide a baseline for comparison. This produced a RMSLE Cross Validation of 0.3530.

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Dataconomy

MARCH 27, 2025

Scikit-learn stands out as a prominent Python library in the machine learning realm, providing a versatile toolkit for data scientists and enthusiasts alike. Its comprehensive functionality caters to various tasks, making it a go-to resource for both simple and complex machine learning projects.

Machine Learning 91

Machine Learning Machine Learning Cross Validation Clustering 91

DrivenData Labs

MAY 24, 2023

The NAS is investing in new ways to bring vast amounts of data together with state-of-the-art machine learning to improve air travel for everyone. Federated learning is a technique for collaboratively training a shared machine learning model across data from multiple parties while preserving each party's data privacy.

Machine Learning 225

Machine Learning Machine Learning Data Science Decision Trees 225

DrivenData Labs

JANUARY 22, 2025

Final Stage Overall Prizes where models were rigorously evaluated with cross-validation and model reports were judged by a panel of experts. The cross-validations for all winners were reproduced by the DrivenData team. Lower is better. Unsurprisingly, the 0.10 quantile was easier to predict than the 0.90

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Cross Validation Machine Learning Machine Learning ML 147

Dataconomy

MARCH 17, 2025

Predictive modeling plays a crucial role in transforming vast amounts of data into actionable insights, paving the way for improved decision-making across industries. By leveraging statistical techniques and machine learning, organizations can forecast future trends based on historical data.

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Decision Trees Predictive Analytics Data Preparation Machine Learning 91

Pickl AI

JANUARY 8, 2025

Summary : Feature selection in Machine Learning identifies and prioritises relevant features to improve model accuracy, reduce overfitting, and enhance computational efficiency. Introduction Feature selection in Machine Learning is identifying and selecting the most relevant features from a dataset to build efficient predictive models.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Support Vector Machines 52

Mlearning.ai

APRIL 13, 2023

Photo by Robo Wunderkind on Unsplash In general , a data scientist should have a basic understanding of the following concepts related to kernels in machine learning: 1. Machine learning algorithms rely on mathematical functions called “kernels” to make predictions based on input data.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Support Vector Machines 52

Pickl AI

JANUARY 3, 2024

Feature engineering in machine learning is a pivotal process that transforms raw data into a format comprehensible to algorithms. Through Exploratory Data Analysis , imputation, and outlier handling, robust models are crafted. Hence, it is important to discuss the impact of feature engineering in Machine Learning.

Machine Learning 52

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Pickl AI

NOVEMBER 2, 2023

A cheat sheet for Data Scientists is a concise reference guide, summarizing key concepts, formulas, and best practices in Data Analysis, statistics, and Machine Learning. What are Cheat Sheets in Data Science? It includes data collection, data cleaning, data analysis, and interpretation.

Data Scientist 52

Data Scientist Data Science Data Visualization Machine Learning 52

Pickl AI

JULY 26, 2023

The concepts of bias and variance in Machine Learning are two crucial aspects in the realm of statistical modelling and machine learning. Understanding these concepts is paramount for any data scientist, machine learning engineer, or researcher striving to build robust and accurate models.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

Pickl AI

SEPTEMBER 18, 2024

Summary: Machine Learning Engineer design algorithms and models to enable systems to learn from data. Introduction Machine Learning is rapidly transforming industries. Who is a Machine Learning Engineer? They ensure that Machine Learning solutions are accurate, scalable, and maintainable.

Machine Learning 52

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Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. billion by 2031, growing at a CAGR of 34.20%.

Machine Learning 52

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Pickl AI

JUNE 4, 2024

Summary : Building a machine learning model is just one step. Validating its performance on unseen data is crucial. Python offers various tools like train-test split and cross-validation to assess model generalizability. This helps identify overfitting and select the best model for real-world use.

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Cross Validation Python Machine Learning Machine Learning 52

Mlearning.ai

JUNE 6, 2023

In recent years, the field of machine learning has gained tremendous momentum, offering powerful solutions and valuable insights from vast amounts of data. However, the process of building machine learning models traditionally involved a time-consuming and resource-intensive approach, requiring extensive expertise.

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Pickl AI

NOVEMBER 18, 2024

Summary: The blog provides a comprehensive overview of Machine Learning Models, emphasising their significance in modern technology. It covers types of Machine Learning, key concepts, and essential steps for building effective models. The global Machine Learning market was valued at USD 35.80

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Flipboard

FEBRUARY 2, 2023

With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their knowledge of the games within the game of football. Models were trained and cross-validated on the 2018, 2019, and 2020 seasons and tested on the 2021 season.

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Towards AI

AUGUST 23, 2024

Many data scientists I’ve spoken with agree that LLMs represent the future, yet they often feel that these models are too complex and detached from the everyday challenges faced in enterprise environments. Last Updated on September 2, 2024 by Editorial Team Author(s): Ori Abramovsky Originally published on Towards AI.

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Pickl AI

MARCH 26, 2024

Summary: The KNN algorithm in machine learning presents advantages, like simplicity and versatility, and challenges, including computational burden and interpretability issues. Nevertheless, its applications across classification, regression, and anomaly detection tasks highlight its importance in modern data analytics methodologies.

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Pickl AI

DECEMBER 10, 2024

Summary: Feature extraction in Machine Learning is essential for transforming raw data into meaningful features that enhance model performance. Understanding techniques, such as dimensionality reduction and feature encoding, is crucial for effective data preprocessing and analysis. The global market was valued at USD 36.73

Machine Learning 52

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AWS Machine Learning Blog

JANUARY 26, 2023

Amazon SageMaker is a fully managed machine learning (ML) service providing various tools to build, train, optimize, and deploy ML models. Data scientists train multiple ML algorithms to examine millions of consumer data records, identify anomalies, and evaluate if a person is eligible for credit.

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ML ML Data Scientist AWS 101

Pickl AI

AUGUST 29, 2023

Data Scientists are highly in demand across different industries for making use of the large volumes of data for analysisng and interpretation and enabling effective decision making. One of the most effective programming languages used by Data Scientists is R, that helps them to conduct data analysis and make future predictions.

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Towards AI

AUGUST 11, 2023

Tabular data has been around for decades and is one of the most common data types used in data analysis and machine learning. Traditionally, tabular data has been used for simply organizing and reporting information. The synthetic datasets were created using a deep-learning generative network called CTGAN.[3]

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Mlearning.ai

NOVEMBER 30, 2023

Mastering Tree-Based Models in Machine Learning: A Practical Guide to Decision Trees, Random Forests, and GBMs Image created by the author on Canva Ever wondered how machines make complex decisions? Just like a tree branches out, tree-based models in machine learning do something similar. Let’s get started!

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Machine Learning Machine Learning Decision Trees Data Science 52

AWS Machine Learning Blog

OCTOBER 3, 2024

Steamlining model management and deployment with SageMaker Amazon SageMaker is a managed machine learning platform that provides data scientists and data engineers familiar concepts and tools to build, train, deploy, govern , and manage the infrastructure needed to have highly available and scalable model inference endpoints.

Data Science 95

Data Science AWS Machine Learning Machine Learning 95

DrivenData Labs

MAY 22, 2024

Meet the Winners ¶ Prize Name 1st place Rasyid Ridha (rasyidstat) 2nd place Roman Chernenko and Vitaly Bondar (Team ck-ua) 3rd place Matthew Aeschbacher (oshbocker) Rasyid Ridha ¶ Place: 1st Prize: $25,000 Home country: Indonesia Username: rasyidstat Background: Experienced Data Scientist specializing in time series and forecasting.

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Pickl AI

APRIL 21, 2025

Widely used in image segmentation, speech recognition, and anomaly detection, GMM is essential for complex Data Analysis. Introduction The Gaussian Mixture Model (GMM) stands as one of the most powerful and flexible tools in the field of unsupervised Machine Learning and statistics.

Clustering 52

Clustering Algorithm Machine Learning Machine Learning 52

DrivenData Labs

MARCH 28, 2023

I am involved in an educational program where I teach machine and deep learning courses. Machine learning is my passion and I often take part in competitions. S1 and S2 features and AGBM labels were carefully preprocessed according to statistics of training data. What motivated you to compete in this challenge?

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Machine Learning Machine Learning Cross Validation Deep Learning 130

Ocean Protocol

NOVEMBER 28, 2024

Introduction The Formula 1 Prediction Challenge: 2024 Mexican Grand Prix brought together data scientists to tackle one of the most dynamic aspects of racing — pit stop strategies. With every second on the track critical, the challenge showcased how data can shape decisions that define race outcomes.

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Cross Validation Decision Trees Data Scientist Data Science 85

Heartbeat

OCTOBER 23, 2023

Revolutionizing Healthcare through Data Science and Machine Learning Image by Cai Fang on Unsplash Introduction In the digital transformation era, healthcare is experiencing a paradigm shift driven by integrating data science, machine learning, and information technology.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Big Data Analytics 52

Heartbeat

FEBRUARY 20, 2023

First-time project and model registration Photo by Isaac Smith on Unsplash The world of machine learning and data science is awash with technicalities. Machine learning problems could grow to such an extent that you constantly lose track of what you are doing. The fix around this is model tracking.

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