Cloudera

JULY 15, 2019

At a time when machine learning, deep learning, and artificial intelligence capture an outsize share of media attention, jobs requiring SQL skills continue to vastly outnumber jobs requiring those more advanced skills. This sequence of courses teaches the essential skills for working with data of any size using SQL.

Education 59

Education Big Data Data Analysis SQL 59

Knowledge Hut

NOVEMBER 17, 2023

Amazon RDS allows access to several acquainted database engines, including Amazon Aurora, MySQL, PostgreSQL, MariaDB, Oracle, and SQL Server. Amazon Aurora Amazon Aurora is well-suited to MySQL and PostgreSQL relational databases and is used to combine the presentation and accessibility of high-end profitable databases.

AWS 52

AWS Amazon Web Services PostgreSQL Relational Database 52

ProjectPro

JANUARY 19, 2022

Good knowledge of various machine learning and deep learning algorithms will be a bonus. Machine Learning and Deep Learning Understanding machine learning and deep learning algorithms aren’t a must for data engineers. For machine learning, an introductory text by Gareth M.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Knowledge Hut

NOVEMBER 2, 2023

In this, there are options for SQL Server, Oracle, MariaDB, MySQL, PostgreSQL, and Amazon Aurora. Developers can use AWS services for building smart apps that rely on complex algorithms and machine learning technology. For MXNet and TensorFlow, there are deep learning development frameworks offered by AWS.

Amazon Web Services 52

Amazon Web Services AWS IT Transportation 52

Databand.ai

DECEMBER 20, 2022

Olga is skilled in MySQL, PostgreSQL, and R and regularly publishes articles on topics like data analysis and machine learning. Follow Olga on LinkedIn 13) Richmond Alake Machine Learning Architect at Slalom Build Richmond is Machine Learning Architect and a Machine Learning Content Creator.

Data Analytics 52

Data Analytics Google Cloud Data Science Machine Learning 52

Databand.ai

DECEMBER 13, 2022

In fact, he has experience in almost all aspects of the data life cycle, from dashboards, analytics, and statistical tests to setting up servers, building machine learning pipelines, and data warehouses. Furthermore, he is experienced in most types of datasets having built deep learning models in NLP, CV, and RL tasks.

Data Engineering 52

Data Engineering Data Engineer Engineering AWS 52

AltexSoft

JUNE 26, 2023

When developing machine learning models, you need several years’ worth of historical data (two-three years, at the very minimum), complemented with current information. Deep learning models consume even more — tens and hundreds of thousands of samples. They won’t make accurate predictions if trained on small datasets.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52