XDJ [Download] Machine Learning with Imbalanced Data For Free What you’ll learn Apply random under-sampling to remove observations from majority classes Perform under-sampling by removing observations that are hard to classify Carry out under-sampling by retaining observations at the boundary of class separation Apply random over-sampling to augment the minority class Create syntethic data to increase the examples of the minority class Implement SMOTE and its variants to synthetically generate data Use ensemble methods with sampling techniques to improve model performance Change the miss-classification cost optimized by the models to accomodate minority classes Determine model performance with the most suitable metrics for imbalanced datasets Requirements Knowledge of machine learning basic algorithms, i.e., regression, decision trees and nearest neighbours Python programming, including familiarity with NumPy, Pandas and Scikit-learn A Python and Jupyter notebook installation Who this course is for: Data scientists and machine learning engineers working with imbalanced datasets Data scientists who want to improve the performance of models trained on imbalanced datasets Students who want to learn intermediate content on machine learning Students working with imbalanced multi-class targets SALES PAGE DOWNLOAD LINK RAR password: xdj@hacksnation.com
jocadi Can you please provide the updated version of this course. there are some videos missing Thanks in advanced !!! 😍
SandrinePonsaqw 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
Valeriefgdf updated Download links ( free ): 1: https://drive.google.com/drive/folders/ljhel1QgzfgzMhvLO 2: https://mega.nz/file/hidfnbhytxIvbnhGZr2qvGx 4: https://mega.nz/file/lhduhscvf8fJIfgthjklm
