Covers: theory of Logistic Regression

- What is Wrong with Linear Regression for Classification?
- How can logistic regression results be interpreted?
- What are the advantages and disadvantages of Logistic Regression?

Read section 4.2 of the book and follow the proposed example

Christoph Molnar

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Collaborators

- Objectives
- Understand the concept of Logistic Regression
- Potential Use Cases
- Deep Learning Mathematical Foundations
- Who is this for ?
- BEGINNERDeep Learning practitioners new to Mathematical foundations

Click on each of the following **annotated items** to see details.

OTHER 1. Sigmoid Function

- How does sigmoid curve look like?
- What's the derivative of the Sigmoid function?
- What's the integral of the Sigmoid function?

10 minutes

VIDEO 2. Regression

- Why is regression useful?
- What's the equation for a linear regression?
- How does linear regression works?

13 minutes

ARTICLE 3. Logistic Regression

- What is logistic regression?
- How can I use logistic regression with an example?
- How can I use logistic regression in Python (Sklearn)?

15 minutes

ARTICLE 4. Logistic Regression

- What is Wrong with Linear Regression for Classification?
- How can logistic regression results be interpreted?
- What are the advantages and disadvantages of Logistic Regression?

15 minutes

BOOK_CHAPTER 5. Logistic Regression

- What is the difference between naïve Bayes and Logistic Regression?
- What is logistic regression?
- What advantages has the sigmoid function?

15 minutes

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