Clustering and Dimensionality Reduction
Unlock the Power of Unsupervised Machine Learning and Data Mining with Cluster Analysis and Dimensionality Reduction
CourseBy Dalibor Veljkovic
Forecasting with Machine Learning
Forecast single and multiple time series with machine learning models. Implement backtesting to evaluate models before deployment.
CourseBy Kishan Manani
Feature Engineering for Time Series Forecasting
Create lag, window and seasonal features, perform imputation and encoding, extract datetime variables, remove outliers, and more.
CourseBy Kishan Manani
Feature Engineering for Machine Learning
Learn imputation, variable encoding, discretization, feature extraction, how to work with datetime, outliers, and more.
CourseBy Soledad Galli
Feature Selection for Machine Learning
Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling & more.
CourseBy Soledad Galli
Machine Learning Interpretability
Explain interpretable and black box models with LIME, Shap, partial dependency plots and more.
CourseBy Soledad Galli
Hyperparameter Optimization for Machine Learning
Learn grid and random search, Bayesian optimization, multi-fidelity models, Optuna, Hyperopt, Scikit-Optimize & more.
CourseBy Soledad Galli
Machine Learning with Imbalanced Data
Learn to over-sample and under-sample your data, apply SMOTE, ensemble methods, and cost-sensitive learning.
CourseBy Soledad Galli
7 Takes on Working with Imbalanced Data - FREE
Sole discusses three recent articles that shift the conversation around handling imbalanced datasets. These articles explore whether generating synthetic data is effective with current state-of-the-art algorithms, and examine the implications of using synthetic data on model performance and deployment.
Digital file1 fileBy Soledad Galli
Feature Selection in Machine Learning
Learn how to implement various feature selection methods in just a few lines of code to train faster, simpler, and more reliable machine learning models. Using Python open-source libraries, you will learn how to identify the most predictive features from your data through filter, wrapper, embedded, and other feature selection methods. You'll learn the advantages and limitations of each method, and be ready to choose the best one based on your data and the model you want to train.
Digital file2 filesBy Soledad Galli
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