KDnuggets

DECEMBER 10, 2019

From not sweating missing values, to determining feature importance for any estimator, to support for stacking, and a new plotting API, here are 5 new features of the latest release of Scikit-learn which deserve your attention.

Data Preparation 119

Data Preparation Machine Learning Python Data 119

KDnuggets

NOVEMBER 7, 2019

In this tutorial, we show how to apply mathematical set operations (union, intersection, and difference) to Pandas DataFrames with the goal of easing the task of comparing the rows of two datasets.

Datasets 97

Datasets Data Preparation Data Science Python 97

KDnuggets

DECEMBER 13, 2019

We show how to build intuitive and useful pipelines with Pandas DataFrame using a wonderful little library called pdpipe.

Building 114

Building Data Preparation Python Data 114

ProjectPro

JANUARY 19, 2022

Data engineers will be in high demand as long as there is data to process. According to Dice Insights, data engineering was the top trending career in the technology industry in 2019, beating out computer scientists, web designers, and database architects. This real-world data engineering project has three steps.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

ProjectPro

JANUARY 15, 2021

Free access to solved machine learning Python and R code examples can be found here (these are ready-to-use for your projects) 7) Common Objects in Context (COCO) Dataset With a total of 330K images, over 200K labeled 91 stuff categories, 80 object categories, 1.5

Datasets 40

Datasets Machine Learning Retail Banking 40

AltexSoft

MAY 14, 2021

As one of the key players in the world of Big Data distributed processing, Apache Spark is developer-friendly as it provides bindings to the most popular programming languages used in data analysis like R and Python. Talend is considered one of the most effective and easy-to-use data integration tools focusing on Big Data.

Big Data 78

Big Data Data Analytics IT NoSQL 78

AltexSoft

DECEMBER 15, 2021

Namely, AutoML takes care of routine operations within data preparation, feature extraction, model optimization during the training process, and model selection. In the meantime, we’ll focus on AutoML which drives a considerable part of the MLOps cycle, from data preparation to model validation and getting it ready for deployment.

Machine Learning 59

Machine Learning Deep Learning Algorithm Telecommunication 59