Machine Learning with Python

SpiralTrain BV

In the course Machine Learning with Python participants learn how to implement machine learning algorithms using Python and the Scikit-learn library.

Price

from €2,999

Level

Not specified

Kind

Training

Subject

Automation and IT, Training and education

More information and registration at SpiralTrain BV

Description

Machine Learning Intro The Machine Learning with Python course starts with an overview of the basic concepts of Machine Learning in which models are made on the basis of supplied data. The difference is explained between Supervised and Unsupervised Learning. Scikit-learn Library Subsequently the libraries that form the foundation behind Machine Learning with Scikit-learn such as Numpy, Pandas, MatPlotLib and Seaborn are discussed. In the basic architecture of Scikit-learn, the data is split into a feature matrix and a target array. Also treated is how a model is trained with a training set and then compared to a test set with the Estimator API. Feature Handling The course Machine Learning with Python also includes Feature Engineering. This discusses how to deal with categorical features, text features, image features and derived features. And the use of features pipelines is also explained. Regressions After a treatment of the Naive Bayes theorem with Naive Bayes classifiers and the models based on them, Linear and Logistic regression are discussed. Specialist versions such as Polynomial Regression, Ridge Regression and Lasso Regularization are also covered.