Hands-on projects and exercises tailored to reinforce key concepts and build practical experience. From basic algorithms to real-world applications of machine learning .
Topic for Reel: Regularization in XGBoost: Explain regularization techniques used in XGBoost, such as shrinkage (learning rate) and tree pruning, to prevent overfitting and improve generalization performance.
The purpose of giving you this task is to:
- Boost your skills and knowledge.
- Put what you've learned into practice.
- Encourage you to be creative.
The purpose of giving you this task is to:
- Enhance your data manipulation and preprocessing skills.
- Apply data cleaning techniques to prepare datasets for analysis.
- Ensure you understand the importance of data quality in machine learning projects.
Objective: The objective of this project is to prepare a dataset for use in a predictive model by performing necessary data cleaning and preprocessing steps. This involves handling missing values, removing duplicates, and transforming data into a suitable format for analysis. By the end of this task, you will have a clean and well-structured dataset that can be used to train reliable machine learning models for disease classification and prognosis.
The purpose of giving you this task is to:
- Develop and enhance your skills in time series analysis and market price forecasting.
- Apply advanced machine learning techniques to predict market prices for various commodities.
- Understand the impact of data preprocessing, feature engineering, and model selection on prediction accuracy.
Objective: Develop a robust time series machine learning model to accurately forecast future market prices for various commodities based on historical data. This involves cleaning and preprocessing the data, analyzing and engineering features, and selecting and fine-tuning suitable forecasting models.
