Hyperparameter Optimization for Machine Learning For Free

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Ruchika oberoi

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Mar 27, 2022
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[Download] Hyperparameter Optimization for Machine Learning For Free

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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


RAR password: [email protected]