[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