Pickl AI

MAY 29, 2024

Mastering programming, statistics, Machine Learning, and communication is vital for Data Scientists. A typical Data Science syllabus covers mathematics, programming, Machine Learning, data mining, big data technologies, and visualisation. Python and R are popular due to their extensive libraries and ease of use.

Data Scientist 52

Data Scientist Data Mining Data Science Machine Learning 52

AWS Machine Learning Blog

JUNE 17, 2024

The training data used for this pipeline is made available through PrestoDB and read into Pandas through the PrestoDB Python client. The queries that are used to fetch data at training and batch inference steps are configured in the config file.

ML 114

ML Machine Learning Data Science Big Data 114

Pickl AI

JUNE 21, 2023

Learn programming languages and tools: While you may not have a technical background, acquiring programming skills is essential in data science. Start by learning Python or R, which are widely used in the field. Also, learn how to analyze and visualize data using libraries such as Pandas, NumPy, and Matplotlib.

Data Scientist 52

Data Scientist Data Science Python Continuous Learning 52

Bugra Akyildiz

JUNE 29, 2024

This week, I will cover why I think data janitor work is dying and companies that are built in on top of data janitor work could be ripe for disruption through LLMs and what to do about it. A data janitor is a person who works to take big data and condense it into useful amounts of information.

LLM 40

LLM Big Data Data Quality ETL 40

Pickl AI

AUGUST 21, 2024

How Web Scraping Works Target Selection : The first step in web scraping is identifying the specific web pages or elements from which data will be extracted. Data Extraction: Scraping tools or scripts download the HTML content of the selected pages. This targeted approach allows for more precise data collection.

Data Extraction 52

Data Extraction Automation Data Quality Data Analysis 52

The MLOps Blog

MARCH 15, 2023

Provides data security using AI & blockchain technologies. Automates data collection from varied sources using extraction modules. Data extraction, model training, and storage all served under one roof. Ensures data protection and leaks by ensuring best practices for data storage.

ETL 59

ETL Categorization Data Integration Automation 59

Pickl AI

JUNE 7, 2024

Impact on Data Quality and Business Operations Using an inappropriate ETL tool can severely affect data quality. Poor data quality can lead to inaccurate business insights and decisions. Data extraction, transformation, or loading errors can result in data loss or corruption.

ETL 40

ETL Data Integration Data Quality Metadata 40

Dlabs.ai

DECEMBER 6, 2022

They use various state-of-the-art technologies, such as statistical modeling, neural networks, deep learning, and transfer learning to uncover the underlying relationships in data. launched an initiative called ‘ AI 4 Good ‘ to make the world a better place with the help of responsible AI.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Computer Vision Machine Learning 52