Regression Machine Learning with Python

Regression Machine Learning with Python, Learn regression machine learning from basic to expert level through a practical course with Python programming language

• Created by Diego Fernandez
• 6 hours on-demand video
• 10 articles
• 17 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion
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What you'll learn

• Read S&P 500® Index ETF prices data and perform regression machine learning operations by installing related packages and running code on Python IDE.
• Create target and predictor algorithm features for supervised regression learning task.
• Select relevant predictor features subset through Student t-test, ANOVA F-test, false discovery rate and family-wise error rate univariate filter methods.
• Choose relevant predictor features subset through recursive feature elimination deterministic wrapper method.
• Designate relevant predictor features subset through least absolute shrinkage and selection operator embedded method.
• Extract predictor features transformations through principal component analysis.
• Train algorithm for mapping optimal relationship between target and predictor features.
• Test algorithm for evaluating previously optimized relationship forecasting accuracy through mean absolute error and root mean squared error scale-dependent metrics.
• Calculate generalized linear models such as linear regression or Ridge regression and select optimal linear regression coefficients regularization parameter through time series cross-validation.
• Compute similarity methods such as k nearest neighbors and select optimal number of nearest neighbors parameter through time series cross-validation.
• Estimate frequency methods such as decision tree and select optimal maximum tree depth parameter through time series cross-validation.
• Calculate ensemble methods such as random forest or gradient boosting machine and select optimal maximum trees depth parameter through time series cross-validation.
• Compute maximum margin methods such as linear or non-linear support vector machines and select optimal error term penalization parameter through time series cross-validation.
• Estimate multi-layer perceptron methods such as artificial neural network and select optimal node connection weight decay regularization parameter through time series cross-validation.
• Compare regression machine learning algorithms training and testing.

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