Learn deep learning regression from basic to expert level through a practical course with Python programming language.
Master key concepts and practical skills through structured learning modules. By completing this curriculum, you'll gain valuable expertise applicable to real-world scenarios.
Read or download S&P 500® Index ETF prices data and perform deep learning regression 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 and ANOVA F-test univariate filter methods and extract predictor features transformations through principal component analysis.
Train algorithm for mapping optimal relationship between target and predictor features through artificial neural network, deep neural network and recurrent neural network.
Regularize algorithm learning through nodes connections weight decay, visible or hidden layers dropout fractions and stochastic gradient descent algorithm learning rate.
This comprehensive Deep Learning Regression with Python curriculum is designed to take you from foundational concepts to advanced implementation. Each module builds upon the previous, ensuring a structured learning path that maximizes knowledge retention and practical application.
Understand what you need to succeed in this course and determine if it's the right fit for your learning goals
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Everything you need to know about this online course, from duration to certification
Course Instructor
Diego Fernandez
Expert instructor with industry experience
Course Language
English
All materials in English
This online course offers comprehensive training with expert instruction, practical exercises, and a certificate of completion. Join thousands of students advancing their careers through quality online education.
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