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Instructor
Charles Arthur, Instructor - Supervised Learning - Linear Regression in Python

Charles Arthur

Is the Director of Data Operations at Nielson & has 10+ years of Data Science experience working in different industries like CPG, Telecom, Internet Advertising, and Insurance; he also helped build an ML SaaS startup. Charles holds an MBA from Cornell University & an MS in Data Science from Northwestern University and a BS in Mathematics from UCSD.
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Instructor: Charles Arthur

Modeling linear data with statistics and Machine Learning

  • Learn and understand the applications of least squares.
  • Understand model selection and fit on real world applications.
  • Instructor has more than 10 years of experience with an MBA from Cornel University & an MS in Data Science from Northwestern University.

Course 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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What am I going to get from this course?

Students will learn and understand the applications of the following algorithms:
  • Least squares
  • Penalized least squares
  • Non-parametric methods
  • Model selection and fit on real world applications:
    • Insurance
    • Healthcare
    • etc.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

Programming Knowledge:
  • Python (preferred)
  • Scikit-Learn exposure
  • Basic Statistics
  • Exposure to Linear Algebra
  • Completed: Course 1
    •  Machine Learning for Predictive Data Analytics

Who should take this course? Who should not?

This course is best for practitioners that have exposure to basic statistics and are interested in machine learning. 

Managers who would like exposure to linear algorithms would also benefit from this material.

Curriculum

Module 1: Introduction

Lecture 1 Introduction

Introduction to Supervised Linear Regression Course - Prerequisites - Skills gained

Module 2: Introduction to Machine Learning and Supervised Regression

Lecture 2 Introduction to Machine Learning and Supervised Regression

- Discuss the overall AI ecosystem and how Machine Learning (ML) is part of that ecosystem. - Understand the 3 different types of algorithms that make up ML - Provide some intuition for why functions and optimizations are important in ML. - Differences between Statistical and ML approaches to supervised linear regression.

Quiz 1 Module 2 - ML and Supervised Regression

Module 3: Machine Learning - Understanding assuptions

Lecture 3 Machine Learning - Understanding Assuptions

- Survey the statistical concepts important to understanding Linear Algorithms. - Design of experiments. - Conducting experiments. - Understand the difference between linear and non-linear functions.

Quiz 2 Module 3 - Linear Regression Assumptions

Module 4: Least Squares Regression - Ordinary Regression

Lecture 4 Least Squares Regression - Ordinary Regression

Develop the simple linear regression algorithm. Understand the basic linear regression assumptions. Learn to identify when assumption violations occur. Understand how to evaluate model output.

Quiz 3 Module 4 - Simple Regression

Module 5: Least Squares Regression - Multiple Regression

Lecture 5 Least Squares Regression - Multiple Regression

Extend the Least Squares algorithm to multiple dimensions Explore data to understand variable importance Prepare data for multiple regression Optimizing between Bias and Variance

Quiz 4 Module 5 - Multiple Regression

Module 6: Penalized Regression - L1/L2 Optimization

Lecture 6 Penalized Regression - L1/L2 Optimization

Understand motivation behind penalized regression Optimize L1 Regression (Lasso) parameters Optimize L2 Regression (Ridge) parameters Combine the L1/L2 penalties (Elastic Net) Understand the difference and trade offs between Subset Selection and Shrinkage Optimize hyper-parameters with Cross-Validation

Quiz 5 Module 6 - Penalized Regression

Module 7: Kernel Methods - Support Vector Machines

Lecture 7 Kernel Methods - Support Vector Machines

Understand theory and motivation behind kernel methods. Derive a basic kernel and use the kernel trick. Build a support vector classifier. Extend to regression with support vector machine. Optimize parameters with Cross validation and Grid Search

Quiz 6 Module 7 - Support Vector Machines

Module 8: Kernel Methods - Gaussian Process Regression

Lecture 8 Kernel Methods - Gaussian Process Regression

Understand multivariate distributions and non-parametric regression. Use Bayesian probability with joint probabilities. Develop theory behind Gaussian Process Regression. Optimize kernels and hyper-parameters.

Quiz 7 Module 8 - Gaussian Process Regression

Module 9: Summary and Real World Applications

Lecture 9 Summary and Real World Applications

Review Supervised Linear Regression topics. Perform Linear regression on real world data.

Quiz 8 Case Study

Download real world dataset and perform regressions and validations to minimize mean square error on the predictions.