Analytics Vidhya

OCTOBER 14, 2020

Overview Learn about the integration capabilities of Power BI with Azure Machine Learning (ML) Understand how to deploy machine learning models in a production. The post The Power of Azure ML and Power BI: Dataflows and Model Deployment appeared first on Analytics Vidhya.

Power BI 271

Power BI Azure ML ML 271

Towards AI

APRIL 7, 2024

This involves visualizing the data and analyzing key statistics.

Azure 105

Azure Machine Learning Machine Learning Exploratory Data Analysis 105

ODSC - Open Data Science

JULY 19, 2023

This resulted in a wide number of accelerators, code repositories, or even full-fledged products that were built using or on top of Azure Machine Learning (Azure ML). The Azure data platforms in this diagram are neither exhaustive nor prescriptive. Creation of Azure Machine Learning workspaces for the project.

Machine Learning 52

Machine Learning Machine Learning Azure Data Science 52

Towards AI

JUNE 25, 2024

It is widely supported by platforms like GCP and Azure, as well as Databricks, which was founded by the creators of Spark. This is the first one, where we look at some functions for data quality checks, which are the initial steps I take in EDA. Let’s get started. 🤠 🔗 All code and config are available on GitHub.

ML 80

ML ML EDA Data Wrangling 80

Mlearning.ai

MAY 23, 2023

This includes skills in data cleaning, preprocessing, transformation, and exploratory data analysis (EDA). Check out this course to upskill on Apache Spark —  [link] Cloud Computing technologies such as AWS, GCP, Azure will also be a plus. Familiarity with libraries like pandas, NumPy, and SQL for data handling is important.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Becoming Human

MAY 15, 2023

For Data Analysis you can focus on such topics as Feature Engineering , Data Wrangling , and EDA which is also known as Exploratory Data Analysis. First learn the basics of Feature Engineering, and EDA then take some different-different data sheets (data frames) and apply all the techniques you have learned to date.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

Mlearning.ai

JULY 11, 2023

For example, when it comes to deploying projects on cloud platforms, different companies may utilize different providers like AWS, GCP, or Azure. Therefore, having proficiency in a specific cloud platform, such as Azure, does not mean you will exclusively work with that platform in the industry.

Data Science 40

Data Science Exploratory Data Analysis Azure Power BI 40