Udemy - Imbalanced Classification Master Class in Python torrent

Imbalanced Classification Master Class in Python



MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 88 lectures (3h 6m) | Size: 861.2 MB
A Step-by-Step Guide to Handling Real-World Class Imbalance in Machine Learning
What you'll learn:
How to use data sampling algorithms like SMOTE to transform the training dataset for an imbalanced dataset when fitting a range of machine learning models
How algorithms from the field of cost-sensitive learning can be used for imbalanced classification
How to use modified versions of standard algorithms like SVM and decision trees to take the class weighting into account
How to tune the threshold when interpreting predicted probabilities as class labels
How to calibrate probabilities predicted by nonlinear algorithms that are not fit using a probabilistic framework
How to use algorithms from the field of outlier detection and anomaly detection for imbalanced classification
How to use modified ensemble algorithms that have been modified to take the class distribution into account during training
How to systematically work through an imbalanced classification predictive modeling project

Requirements
You'll need a solid foundation in machine learning
You'll need a solid background in Python
A familiarity with classification problems would be ideal

Description
Welcome to Imbalanced Classification Master Class in Python.

Classification predictive modeling is the task of assigning a label to an example. Imbalanced classification is those classification tasks where the distribution of examples across the classes is not equal. Typically the class distribution is severely skewed so that for each example in the minority class, there may be one hundred or even one thousand examples in the majority class. Practical imbalanced classification requires the use of a suite of specialized techniques, data preparation techniques, learning algorithms, and performance metrics.