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Machine Learning - Linear and Logistic Regression

Indian : Rs.1918
International : $30
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!
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.

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
Chapter I: Introduction
  • Lesson I: You, This Course, & Us!

Chapter II: Connect the Dots with Linear Regression
  • Lesson I: Using Linear Regression to Connect the Dots
  • Lesson II: Two Common Applications of Regression
  • Lesson III: Extending Linear Regression to Fit Non-linear Relationships

Chapter III: Basic Statistics Used for Regression
  • Lesson I: Understanding Mean & Variance
  • Lesson II: Understanding Random Variables
  • Lesson III: The Normal Distribution

Chapter IV: Simple Regression
  • Lesson I: Setting up a Regression Problem
  • Lesson II: Using Simple Regression to Explain Cause-Effect Relationships
  • Lesson III: Using Simple Regression for Explaining Variance
  • Lesson IV: Using Simple Regression for Prediction
  • Lesson V: Interpreting the results of a Regression
  • Lesson VI: Mitigating Risks in Simple Regression

Chapter V: Applying Simple Regression
  • Lesson I: Applying Simple Regression in Excel
  • Lesson III: Applying Simple Regression in R
  • Lesson III: 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: Solving 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
5 hrs
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
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