Machine Learning – Linear & Logistic Regression


Build robust models in Excel, R & Python.

This ’Linear & Logistic Regression’ online training course will teach you how to build robust linear models and do logistic regressions in Excel, R, and Python that will stand up to scrutiny when you apply them to real world situations. Supplemental Materials included!

Length: 5 hrs

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In this Linear & Logistic Regression course, you’ll learn about topics such as: understanding random variables, cause-effect relationships, maximum likelihood estimation, and so much more. Follow along with the experts as they break down these concepts in easy-to-understand lessons.

Course Highlights

Simple Regression :

  • Method of least squares, Explaining variance, Forecasting an outcome
  • Residuals, assumptions about residuals
  • Implement simple regression in Excel, R and Python
  • Interpret regression results and avoid common pitfalls

Multiple Regression :

  • Implement Multiple regression in Excel, R and Python
  • Introduce a categorical variable

Logistic Regression :

  • Applications of Logistic Regression, the link to Linear Regression and Machine Learning
  • Solving logistic regression using Maximum Likelihood Estimation and Linear Regression
  • Extending Binomial Logistic Regression to Multinomial Logistic Regression
  • Implement Logistic regression to build a model stock price movements in Excel, R and Python

Course Requirements

  • No statistics background required. Everything is built up from basic math
  • The models are implemented in Excel, R and Python. Install these environments to follow along with the demos

Target Audience

  • Data analysts who want to move from summarizing data to explaining and prediction
  • Folks aspiring to be data scientists
  • Any business professionals who want to apply Linear regression to solve relevant problems


Sample clip


Chapter 01: Introduction

Lesson 01: You, This Course, & Us!

Chapter 02: Connect the Dots with Linear Regression

Lesson 01: Using Linear Regression to Connect the Dots

Lesson 02: Two Common Applications of Regression

Lesson 03: Extending Linear Regression to Fit Non-linear Relationships

Chapter 03: Basic Statistics Used for Regression

Lesson 01: Understanding Mean & Variance

Lesson 02: Understanding Random Variables

Lesson 03: The Normal Distribution

Chapter 04: Simple Regression

Lesson 01: Setting up a Regression Problem

Lesson 02: Using Simple Regression to Explain Cause-Effect Relationships

Lesson 03: Using Simple Regression for Explaining Variance

Lesson 04: Using Simple Regression for Prediction

Lesson 05: Interpreting the results of a Regression

Lesson 06: Mitigating Risks in Simple Regression

Chapter 05: Applying Simple Regression

Lesson 01: Applying Simple Regression in Excel

Lesson 02: Applying Simple Regression in R

Lesson 03: Applying Simple Regression in Python

Chapter 06: Multiple Regression

Lesson 01: Introducing Multiple Regression

Lesson 02: Some Risks inherent to Multiple Regression

Lesson 03: Benefits of Multiple Regression

Lesson 04: Introducing Categorical Variables

Lesson 05: Interpreting Regression results – Adjusted R-squared

Lesson 06:  Interpreting Regression results – Standard Errors of Coefficients

Lesson 07: Interpreting Regression results – t-statistics & p-values

Lesson 08: Interpreting Regression results – F-Statistic

Chapter 07: Applying Multiple Regression using Excel

Lesson 01: Implementing Multiple Regression in Excel

Lesson 02: Implementing Multiple Regression in R

Lesson 03: Implementing Multiple Regression in Python

Chapter 08: Logistic Regression for Categorical Dependent Variables

Lesson 01: Understanding the need for Logistic Regression

Lesson 02: Setting up a Logistic Regression problem

Lesson 03: Applications of Logistic Regression

Lesson 04: The link between Linear & Logistic Regression

Lesson 05: The link between Logistic Regression & Machine Learning

Chapter 09: Solving Logistic Regression

Lesson 01: Understanding the intuition behind Logistic Regression & the S-curve

Lesson 02: Solving Logistic Regression using Maximum Likelihood Estimation

Lesson 03: Solving Logistic Regression using Linear Regression

Lesson 04: Binomial vs Multinomial Logistic Regression

Chapter 10: Applying Logistic Regression

Lesson 01: Predict Stock Price movements using Logistic Regression in Excel

Lesson 02: Predict Stock Price movements using Logistic Regression in R

Lesson 03: Predict Stock Price movements using Rule-based & Linear Regression

Lesson 04: Predict Stock Price movements using Logistic Regression in Python

Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum

Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum