Supervised Learning - Linear Regression in Python

Description:

In this course you will learn to apply Least Squares regression and it's assumptions to real world data. Then we'll improve on that algorithm with Penalized Regression and non-parametric Kernel methods - Understanding basic statistical modeling and assumptions - Build & Evaluate supervised linear models using: Least squares Penalized least squares Non-parametric methods - Model selection and fit on real world applications: Insurance Healthcare etc. - Code samples https://bitbucket.org/arthuranalytics/experfy_courses/src/master/ Topics: Introduction – Supervised Learning and ML ML Statistics – Understanding Assumptions Least Squares Regression – The ML workhorse Linear Model Evaluation– Assess performance Penalized Regression (L1/L2) – Optimization Kernel Methods (SVM) – Other Distributions Real World Applications

 
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