Data Science Dojo

JULY 5, 2023

The development of a Machine Learning Model can be divided into three main stages: Building your ML data pipeline: This stage involves gathering data, cleaning it, and preparing it for modeling. Cleaning data: Once the data has been gathered, it needs to be cleaned.

Machine Learning 195

Machine Learning Machine Learning EDA Exploratory Data Analysis 195

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

Data Scientist ML ML Machine Learning 156

Smart Data Collective

OCTOBER 17, 2022

Data pipelines automatically fetch information from various disparate sources for further consolidation and transformation into high-performing data storage. There are a number of challenges in data storage , which data pipelines can help address. Choosing the right data pipeline solution.

Data Pipeline 130

Data Pipeline Data Warehouse ETL Exploratory Data Analysis 130

Snorkel AI

MAY 12, 2023

To borrow another example from Andrew Ng, improving the quality of data can have a tremendous impact on model performance. This is to say that clean data can better teach our models. Another benefit of clean, informative data is that we may also be able to achieve equivalent model performance with much less data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

MAY 12, 2023

To borrow another example from Andrew Ng, improving the quality of data can have a tremendous impact on model performance. This is to say that clean data can better teach our models. Another benefit of clean, informative data is that we may also be able to achieve equivalent model performance with much less data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

JULY 3, 2023

As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs. Because of this, I’m always looking for ways to automate and improve our data pipelines. So why should we use data pipelines?

Data Pipeline 52

Data Pipeline Exploratory Data Analysis Data Scientist Machine Learning 52

Snorkel AI

JULY 3, 2023

As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs. Because of this, I’m always looking for ways to automate and improve our data pipelines. So why should we use data pipelines?

Data Pipeline 52

Data Pipeline Exploratory Data Analysis Data Scientist Machine Learning 52