Supervised Learning - Linear Regression in Python


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 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

  • Recommend Us