Train in Data Reviews & Ratings

Train in Data

4.8 out of 5 stars

Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

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Ratings and Reviews 18 Ratings

Total 5 out of 5 stars
ease 5 out of 5 stars
features 5 out of 5 stars
design 4.5 out of 5 stars
support 4.5 out of 5 stars

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What is neoCoder?

An effective solution for developing technology skills that empowers organizations to nurture essential competencies while swiftly integrating new hires. This platform showcases in-demand technology courses designed by leading industry professionals and includes a comprehensive development environment that features advanced tools such as syntax highlighting, an array of keyboard shortcuts, and code completion. Users can participate in engaging courses that promote active skill practice, while team members can enhance their abilities through hands-on coding challenges accompanied by valuable feedback. Additionally, our skill assessments—comprising quizzes, project-based evaluations, and hackathons—identify knowledge deficiencies and emphasize strengths. Our adaptive learning engine ensures that each employee enjoys a tailored educational experience aligned with their individual roles and responsibilities. This methodology not only boosts overall team performance but also nurtures a culture of ongoing development and creativity within the organization, fostering an environment where innovation can thrive. By prioritizing personalized learning paths, companies can maximize their workforce's potential and achieve greater success.

What is Train in Data?

Train in Data is a premier online school dedicated to helping learners master intermediate and advanced machine learning through in-depth Python programming and data science courses. Taught by industry experts who have successfully developed, optimized, and deployed machine learning models in enterprise production settings, the curriculum is designed to bridge theory with practical application. The platform’s unique approach ensures students first develop a strong, intuitive grasp of fundamental machine learning concepts, understanding not just the "how" but the "why." Following theory lessons, students engage in hands-on Python coding, reinforcing their learning and gaining the ability to implement algorithms and troubleshoot issues effectively. Train in Data offers a holistic learning journey, covering theory, coding, and problem-solving skills essential for managing real-world projects end-to-end. The courses focus on providing learners with the confidence and expertise required to design, optimize, and deploy machine learning models in professional environments. Beyond technical skills, the platform fosters critical thinking and problem-solving capabilities. Train in Data’s structured yet flexible curriculum makes it accessible to dedicated learners aiming to advance their careers in data science and machine learning. The school prioritizes delivering practical knowledge that is immediately applicable in industry roles. Ultimately, Train in Data prepares students to become capable, self-sufficient machine learning practitioners ready to excel in the evolving tech landscape.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Trimble Connect

Integrations Supported

Jupyter Notebook
JupyterHub
Matplotlib
NumPy
Python
pandas
scikit-learn

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

$15

Supported Platforms

SaaS

Supported Platforms

iPhone
iPad
Windows
Mac

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

iamneo.ai

Date Founded

2016

Company Location

India

Company Website

neocoder.io

Company Facts

Organization Name

Train in Data

Date Founded

2019

Company Location

Germany

Company Website

trainindata.com

Categories and Features

Hackathon

Not specified

Categories and Features

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