Striim

JUNE 11, 2024

How AI is Upleveling Fraud Detection to Safeguard FinServ Transactions Financial services organizations can leverage advanced machine learning (ML) algorithms and deep learning models to analyze vast amounts of transactional data in real time. The result is immediate, robust fraud detection capabilities.

MongoDB 52

MongoDB MySQL Utilities Algorithm 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

MARCH 14, 2024

Natural Language Processing, Computer Vision, Machine Learning, Robotics, and applications in healthcare, finance, and autonomous systems. Key Technologies Programming languages (Java, Python, C++), databases (MySQL, MongoDB), web development tools, and more. Database Technologies: MySQL, Oracle, MongoDB, etc.

Computer Science 52

Computer Science Programming Language Electronics MongoDB 52

DareData

JANUARY 30, 2023

In the current age of readily available deep learning models and easy model training, the most valuable data scientists are those who are able to focus on the stability and scalability of their models, rather than just their performance on a single machine. Examples of relational databases include MySQL or Microsoft SQL Server.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Knowledge Hut

JUNE 23, 2023

Data engineers make a tangible difference with their presence in top-notch industries, especially in assisting data scientists in machine learning and deep learning. You should be well-versed with SQL Server, Oracle DB, MySQL, Excel, or any other data storing or processing software.

Data Engineering 52

Data Engineering Data Engineer Engineering NoSQL 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

Knowledge Hut

MARCH 19, 2024

Select EC2 accelerated computing instances if you require a lot of processing power and GPU capability for deep learning and machine learning. and a MySQL instance in RDS to hold application data. AWS EC2 use cases consist of: With options for load balancing and auto-scaling, create a fault-tolerant architecture.

AWS 52

AWS Amazon Web Services Hospitality Cloud Computing 52

ProjectPro

JULY 23, 2021

With Amazon Polly, you can use advanced deep learning technologies to carry out accurate conversions. This dataset is now a valuable asset for machine learning, Natural language processing, and deep learning applications. With AWS Lambda & Amazon Polly, you can convert textual information to speech.

AWS 52

AWS Project Amazon Web Services Cloud Computing 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

MAY 12, 2023

It is commonly stored in relational database management systems (DBMSs) such as SQL Server, Oracle, and MySQL, and is managed by data analysts and database administrators. TensorFlow is an open-source machine learning framework accommodating many machine and deep learning algorithms.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

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 Data Mining 52

ProjectPro

JUNE 13, 2018

Project Idea 4: Website Monitoring using AWS Lambda and Aurora This AWS project helps you learn the best practices for website monitoring using AWS services like Lambda, Aurora MySQL, Amazon Dynamo DB, and Kinesis. Key areas Identify the best ML approach for the business problems. Exam registration cost: 300 USD.

AWS 52

AWS Certification Amazon Web Services Cloud Computing 52

ProjectPro

AUGUST 24, 2021

Source Code: Analyse Movie Ratings Data Unlock the ProjectPro Learning Experience for FREE 11) Retail Analytics Project Example For retail stores , inventory levels, supply chain movement, customer demand, sales, etc. You will set up MySQL for table creation and migrate data from RDBMS to Hive warehouse to arrive at the solution.

Data Engineering 52

Data Engineering Data Engineer Coding Project 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

ProjectPro

JULY 28, 2021

You can also develop skills in MySQL or JavaScript. Statistical Language: You should have basic – intermediate knowledge of at least one statistical language, such as R or Python. Data Language: SQL is the most popular data language. Data Visualization: It will be one of the most critical tasks for you to perform as a Data Scientist.

Recruitment 40

Recruitment Data Science Machine Learning NoSQL 40

ProjectPro

AUGUST 21, 2015

i) Data Ingestion – The foremost step in deploying big data solutions is to extract data from different sources which could be an Enterprise Resource Planning System like SAP, any CRM like Salesforce or Siebel , RDBMS like MySQL or Oracle, or could be the log files, flat files, documents, images, social media feeds.

Hadoop 40

Hadoop Big Data Metadata Java 40

U-Next

AUGUST 11, 2022

Mathematics, coding, analysis techniques (Mysql, Hadoop), deep learning, visualization techniques, data manipulation, and business experience are Data Analysts’ top technical abilities. Numerous industry professionals anticipate that deep learning will soon replace the amount of customer support positions.

Data Analytics 40

Data Analytics Data Machine Learning Data Science 40

U-Next

AUGUST 3, 2022

Additionally, they must be able to formulate those questions utilising a variety of tools, including analytic, economic, deep learning, and scientific techniques. Programming skills in Python, R, Mysql, and machine learning methods are needed for Data Scientists, workflow competence in Git and the command-line interface.

Data Science 52

Data Science Retail Computer Science Data Mining 52

ProjectPro

APRIL 20, 2017

Access the solution to The Complete Guide to Start Mining Bitcoin in AWS cloud 9) Hadoop Project: Analysing Airlines Dataset As Artificial Intelligence (AI) tools like Machine Learning, Deep Learning, etc. Followed by MySQL is the Microsoft SQL Server. What will you learn from this Hadoop Project?

Hadoop 40

Hadoop Big Data Coding Project 40