This book develops multivariate predictive or dependency analysis techniques (supervised learning techniques in the modern language of Machine Learning) and more specifically classification techniques from a methodological point of view and from a practical point of view with applications through Python software. The following techniques are studied in depth: Generalised Linear Models (Logit, Probit, Count and others), Decision Trees, Discriminant Analysis, K-Nearest Neighbour (kNN), Support Vector Machine (SVM), Naive Bayes, Ensemble Methods (Bagging, Boosting, Voting, Stacking, Blending and Random Forest), Neural Networks, Multilayer Perceptron, Radial Basis Networks, Hopfield Networks, LSTM Networks, RNN Recurrent Networks, GRU Networks and Neural Networks for Time Series Prediction. These techniques are a fundamental support for the development of Artificial Intelligence.
This book develops multivariate predictive or dependency analysis techniques (supervised learning techniques in the modern language of Machine Learning) and more specifically classification techniques from a methodological point of view and from a practical point of view with applications through Python software. The following techniques are studied in depth: Generalised Linear Models (Logit, Probit, Count and others), Decision Trees, Discriminant Analysis, K-Nearest Neighbour (kNN), Support Vector Machine (SVM), Naive Bayes, Ensemble Methods (Bagging, Boosting, Voting, Stacking, Blending and Random Forest), Neural Networks, Multilayer Perceptron, Radial Basis Networks, Hopfield Networks, LSTM Networks, RNN Recurrent Networks, GRU Networks and Neural Networks for Time Series Prediction. These techniques are a fundamental support for the development of Artificial Intelligence.
Advanced Techniques for Multivariate Data Analysis Using PYTHON. Predictive Models for Classification and Segmentation
Advanced Techniques for Multivariate Data Analysis Using PYTHON. Predictive Models for Classification and Segmentation
Product Details
| BN ID: | 2940181151383 |
|---|---|
| Publisher: | Scientific Books |
| Publication date: | 07/08/2025 |
| Sold by: | Draft2Digital |
| Format: | eBook |
| File size: | 21 MB |
| Note: | This product may take a few minutes to download. |