Adatkezelési tájékoztatónkat itt olvashatod:
Süti beállítások kezelése:
Data Science
A kategória szakmai hitelesítője a(z) Zenitech Consulting .
We welcome people who are interested in data processing and machine learning and want to learn more about supervised and unsupervised learning methods. Also, those who already have basic programming skills, especially in Python, and would like to develop further in this area. Those who like to solve problems using different algorithms and are open to learning modern data processing techniques will also feel comfortable in this category.
About Zenitech
We are our clients’ strategic technology partners – designing, delivering and maintaining creative technology solutions that make a real impact on their business.
We use the latest technologies in our bespoke mobile or custom software development, and are at the forefront of innovative implementations of data science, including machine learning, artificial intelligence and AR/VR/XR solutions.
Click here for our website.
Our LinkedIn page.
Zenitech Company Introduction
What can you expect in this category?
In this category, you will find varied and exciting tasks that will help you gain a deeper understanding of different aspects of machine learning. You can expect to apply basic supervised learning models such as linear regression and decision trees, but we will also cover data processing and feature engineering in detail. In addition, you will have the opportunity to work with different models and algorithms such as decision trees, support vector machines (SVM) and various clustering methods.
Round 1: Basic Supervised Learning
Objective: Assess basic understanding and implementation skills related to supervised learning.
Round 2: Data Preprocessing and Feature Engineering
Objective: Evaluate the capability to preprocess and feature engineer datasets for better ML model performance.
Round 3: Advanced Supervised Learning Models
Objective:Test knowledge and application of more advanced supervised learning techniques.
Round 4: Unsupervised Learning
Objective: Assess the ability to understand and apply unsupervised learning methods.
Round 5: Model Evaluation and Hyperparameter Tuning
Objective: Evaluate knowledge of model evaluation metrics and hyperparameter tuning techniques.
2024-es partnereink
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