Data Science Blog

FEBRUARY 23, 2023

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves.

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Supervised Learning Machine Learning Machine Learning Deep Learning 264

Dataconomy

MAY 16, 2023

They employ statistical and mathematical techniques to uncover patterns, trends, and relationships within the data. Data scientists possess a deep understanding of statistical modeling, data visualization, and exploratory data analysis to derive actionable insights and drive business decisions.

Data Scientist 156

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DrivenData Labs

APRIL 10, 2024

Model architectures : All four winners created ensembles of deep learning models and relied on some combination of UNet, ConvNext, and SWIN architectures. In the modeling phase, XGBoost predictions serve as features for subsequent deep learning models. Test-time augmentations were used with mixed results.

Deep Learning 130

Deep Learning Deep Learning Machine Learning Machine Learning 130

AWS Machine Learning Blog

SEPTEMBER 12, 2023

Email classification project diagram The workflow consists of the following components: Model experimentation – Data scientists use Amazon SageMaker Studio to carry out the first steps in the data science lifecycle: exploratory data analysis (EDA), data cleaning and preparation, and building prototype models.

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Data Science Data Scientist AWS ML 128

PyImageSearch

JANUARY 23, 2023

In this tutorial, you will learn the magic behind the critically acclaimed algorithm: XGBoost. Firstly, we have the definition of the training set, which is refers to the training sample , which has features and labels. We have used packages like XGBoost, pandas, numpy, matplotlib, and a few packages from scikit-learn.

Deep Learning 109

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Heartbeat

MARCH 21, 2023

Understanding Model Monitoring Model monitoring is the process of continuously monitoring the performance of a machine-learning model over time. For time series data, model monitoring typically involves tracking a set of performance metrics over time to detect any changes or anomalies in the data that may impact the model’s accuracy.

Exploratory Data Analysis 59

Exploratory Data Analysis EDA Machine Learning Machine Learning 59

Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. This section delves into its foundational definitions, types, and critical concepts crucial for comprehending its vast landscape.

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