Dataconomy

JUNE 13, 2023

It leverages the power of technology to provide actionable insights and recommendations that support effective decision-making in complex business scenarios. At its core, decision intelligence involves collecting and integrating relevant data from various sources, such as databases, text documents, and APIs.

Power BI 103

Power BI Artificial Intelligence Artificial Intelligence Data Analysis 103

phData

MAY 12, 2023

It’s a highly versatile tool, supporting various data types, from simple Excel files to complex databases or big data technologies. It starts with KNIME, which can directly connect to your Snowflake data warehouse using its dedicated database Snowflake connector node. Oh–and it’s free.

Data Warehouse 72

Data Warehouse Machine Learning Machine Learning Database 72

Pickl AI

MAY 30, 2024

It systematically collects data from diverse sources such as databases, online repositories, sensors, and other digital platforms, ensuring a comprehensive dataset is available for subsequent analysis and insights extraction. These include databases, APIs, web scraping, and public datasets.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Pickl AI

JULY 18, 2023

SQL: Mastering Data Manipulation Structured Query Language (SQL) is a language designed specifically for managing and manipulating databases. While it may not be a traditional programming language, SQL plays a crucial role in Data Science by enabling efficient querying and extraction of data from databases.

Data Science 52

Data Science SQL Data Scientist Apache Hadoop 52

Dataconomy

JULY 24, 2023

” Data management and manipulation Data scientists often deal with vast amounts of data, so it’s crucial to understand databases, data architecture, and query languages like SQL. It involves developing algorithms that can learn from and make predictions or decisions based on data.

Data Scientist 172

Data Scientist Data Science Data Analyst Machine Learning 172

Pickl AI

APRIL 26, 2024

SQL stands for Structured Query Language, essential for querying and manipulating data stored in relational databases. The SELECT statement retrieves data from a database, while SELECT DISTINCT eliminates duplicate rows from the result set. What are the advantages and disadvantages of decision trees ?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Mlearning.ai

MAY 23, 2023

There are majorly two categories of sampling techniques based on the usage of statistics, they are: Probability Sampling techniques: Clustered sampling, Simple random sampling, and Stratified sampling. Decision trees are more prone to overfitting. Some algorithms that have low bias are Decision Trees, SVM, etc.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

The MLOps Blog

DECEMBER 19, 2022

1 KNN 2 Decision Tree 3 Random Forest 4 Naive Bayes 5 Deep Learning using Cross Entropy Loss To some extent, Logistic Regression and SVM can also be leveraged to solve a multi-class classification problem by fitting multiple binary classifiers using a one-vs-all or one-vs-one strategy. A set of classes sometimes forms a group/cluster.

ML 52

ML ML Algorithm Deep Learning 52

Dataconomy

MARCH 13, 2023

Data can be collected from various sources, such as databases, sensors, or the internet. Algorithms: Algorithms are used to develop AI models that can learn from data and make predictions or decisions. This data could be in the form of structured data (such as data in a database) or unstructured data (such as text, images, or audio).

Artificial Intelligence 98

Artificial Intelligence Artificial Intelligence Natural Language Processing Algorithm 98

DagsHub

APRIL 21, 2024

It offers implementations of various machine learning algorithms, including linear and logistic regression , decision trees , random forests , support vector machines , clustering algorithms , and more. It is commonly used in MLOps workflows for deploying and managing machine learning models and inference services.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

The MLOps Blog

JUNE 6, 2023

A typical pipeline may include: Data Ingestion: The process begins with ingesting raw data from different sources, such as databases, files, or APIs. This is an ensemble learning method that builds multiple decision trees and combines their predictions to improve accuracy and reduce overfitting. Create the ML model.

ML 52

ML ML Cross Validation Machine Learning 52

Iguazio

APRIL 24, 2024

They can provide information, summaries and insights across many fields without the need for external databases in real-time applications. This is important for real-time decision-making tasks, like autonomous vehicles or high-frequency trading. AI Democratization - LLMs democratize access to AI by lowering the entry barrier.

ML 52

ML ML Data Science AI 52