Data Science

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.