XDJ [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 SALES PAGE DOWNLOAD LINK RAR password: xdj@hacksnation.com
SophieAndrebt Updated download links: link-1: https://mega.nz/file/xIvbnhGZrvu02qvGxblQQN18h link-2: https://gofile.io/d/894Bj5gxcree7? Link-3: https://drive.google.com/drive/folders/1QfgzMhvLOCnbhjfm018SAVQ
Sabrinafg updated Download links ( free ): 1: https://drive.google.com/drive/folders/ljhel1QgzfgzMhvLO 2: https://mega.nz/file/hidfnbhytxIvbnhGZr2qvGx 4: https://mega.nz/file/lhduhscvf8fJIfgthjklm
