Machine Learning - Linear and Logistic Regression

Rapid Skillz

Course

Online

£ 24.06 VAT inc.

*Indicative price

Original amount in USD:

$ 30

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Description

  • Type

    Course

  • Level

    Intermediate

  • Methodology

    Online

  • Duration

    Flexible

  • Start date

    Different dates available

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!

Facilities

Location

Start date

Online

Start date

Different dates availableEnrolment now open

About this course

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.

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Subjects

  • MS Excel
  • Interpreting
  • Excel
  • Machine Learning
  • Linear Regression
  • Logistic Regression
  • Basic Statistics
  • Categorical
  • Non-linear Relationships
  • Simple Regression
  • Multiple Regression

Teachers and trainers (1)

Name Name

Name Name

Teacher

Course programme

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

Additional information

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

LENGTH

5 hrs

See related categories

Machine Learning - Linear and Logistic Regression

£ 24.06 VAT inc.

*Indicative price

Original amount in USD:

$ 30

The best Other Operating Systems courses

See all

See all