In this course, you will learn to analyse and solve competitively such predictive modelling tasks.
When you finish this class, you will:
- Understand how to solve predictive modelling competitions efficiently and learn which of the skills obtained can be applicable to real-world tasks.
- Learn how to preprocess the data and generate new features from various sources such as text and images.
- Be taught advanced feature engineering techniques like generating mean-encodings, using aggregated statistical measures or finding nearest neighbors as a means to improve your predictions.
- Be able to form reliable cross validation methodologies that help you benchmark your solutions and avoid overfitting or underfitting when tested with unobserved (test) data.
- Gain experience of analysing and interpreting the data. You will become aware of inconsistencies, high noise levels, errors and other data-related issues such as leakages and you will learn how to overcome them.
- Acquire knowledge of different algorithms and learn how to efficiently tune their hyperparameters and achieve top performance.
- Master the art of combining different machine learning models and learn how to ensemble.
- Get exposed to past (winning) solutions and codes and learn how to read them.
Disclaimer : This is not a machine learning online course in the general sense. This course will teach you how to get high-rank solutions against thousands of competitors with focus on practical usage of machine learning methods rather than the theoretical underpinnings behind them.
- Python: work with DataFrames in pandas, plot figures in matplotlib, import and train models from scikit-learn, XGBoost, LightGBM.
- Machine Learning: basic understanding of linear models, K-NN, random forest, gradient boosting and neural networks.
Do you have technical problems? Write to us: [email protected]