Machine learning : linear & logistic regression online course
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Online
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Course
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Methodology
Online
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Different dates available
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.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
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Subjects
- Interpreting
- Excel
- Statistics
- MS Excel
- Linear Analysis
- Online training
- Machine Learning
- Online
- Online Course
- Regression
- 24x7
Teachers and trainers (1)
Online Course Online Course
12 Month Unlimited On-Demand Online Access to the Course.
Course programme
Course Outline
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
PACKAGE INCLUDES:
Length of Subscription: 12 Months Online On-Demand Access
Running Time: 5 hrs
Platform: Windows & MAC OS
Level: Beginner to Advanced
Project Files: Included
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Machine learning : linear & logistic regression online course