[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
You must be registered for see links
You must be registered for see links
RAR password: xdj@hacksnation.com