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  • [Download] Hyperparameter Optimization for Machine Learning For Free

[Download] Hyperparameter Optimization for Machine Learning For Free

What you’ll learn

  • Hyperparameter tunning and why it matters

  • Cross-validation and nested cross-validation

  • Hyperparameter tunning with Grid and Random search

  • Bayesian Optimisation

  • Tree-Structured Parzen Estimators, Population Based Training and SMAC

  • Hyperparameter tunning tools, i.e., Hyperopt, Optuna, Scikit-optimize, Keras Turner and others

Requirements

  • Python programming, including knowledge of NumPy, Pandas and Scikit-learn

  • Familiarity with basic machine learning algorithms, i.e., regression, support vector machines and nearest neighbours

  • Familiarity with decision tree algorithms and Random Forests

  • Familiarity with gradient boosting machines, i.e., xgboost, lightGBMs

  • Understanding of machine learning model evaluation metrics

  • Familiarity with Neuronal Networks

Who this course is for:

  • Students who want to know more about hyperparameter optimization algorithms

  • Students who want to understand advanced techniques for hyperparameter optimization

  • Students who want to learn to use multiple open source libraries for hyperparameter tuning

  • Students interested in building better performing machine learning models

  • Students interested in participating in data science competitions

  • Students seeking to expand their breadth of knowledge on machine learning

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