Classification pipeline: building and evaluating

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Supervised Learning: Classification Algorithms

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Hydroacoustics for Hydroelectric Installations

Explores hydroacoustic simulation parameters, mass oscillation modeling, and validation errors in hydroelectric installations.

Decision Trees: Classification

Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.

Generalized Linear Regression: Classification

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Receiver-Operator Characteristics: ROC Curves

Explains ROC curves, Precision-Recall curve, RMSLE, and model validation.

Machine Learning: Features and Model Selection

Delves into the significance of features, model evolution, labeling challenges, and model selection in machine learning.

Hydroacoustics for Hydroelectric Installations

Explores valve closing laws, water hammer, and surge tanks in hydroelectric installations.

Image Classification: Overfitting and Accuracy Measures

Explores overfitting and accuracy measures in image classification, emphasizing the importance of model generalization and optimal accuracy.

Linear Regression and Gradient Descent

Covers linear regression, gradient descent, overfitting, and ridge regression among other concepts.

Document Analysis: Topic Modeling

Explores document analysis, topic modeling, and generative models for data generation in machine learning.