Logistic Regression - Is it Linear Regression?

Is it Linear? Why the sigmoid? Let's talk about it. Breaking Linear Regression video: https://www.youtube.com/watch?v=Bu1WCOQpBnM RESOURCES [1] Great Lecture notes to start understanding Logistic Regression: https://pages.stat.wisc.edu/~st849-1/lectures/GLMH.pdf [2] More slightly detailed notes:https://www.stat.cmu.edu/~ryantibs/advmethods/notes/glm.pdf [3] Mathematical answers about logistic regression: https://math.stackexchange.com/questions/3253634/in-the-context-of-a-bernoulli-distribution-what-exactly-the-relationship-is-bet [4] Easy to understand slides: https://www.sjsu.edu/faculty/guangliang.chen/Math261a/Ch13slides-generalized-linear-models.pdf [5] More good notes: http://www2.stat.duke.edu/~sayan/Sta613/2014/lec/lec_jan24.pdf [6] Good explanation on link functions: https://timothy-barry.github.io/posts/2020-07-07-generalized-linear-models/ [7] Good video deriving the logistic function: https://www.youtube.com/watch?v=IkUPaUpJ274 [8] More notes on generalized Linear Models: https://web.pdx.edu/~newsomj/cdaclass/ho_glm.pdf [9] The introduction to statistical learning book is always a good resource:https://hastie.su.domains/ISLR2/ISLRv2_website.pdf [10] Intuition on the distribution of the error term in logistic regression:https://stats.stackexchange.com/questions/124818/logistic-regression-error-term-and-its-distribution [11] Coding using python’s stats_models package: https://www.statsmodels.org/stable/glm.html#module-reference MATH COURSES (7 day free trial) 📕 Mathematics for Machine Learning: https://imp.i384100.net/MathML 📕 Calculus: https://imp.i384100.net/Calculus 📕 Statistics for Data Science: https://imp.i384100.net/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 Linear Algebra: https://imp.i384100.net/LinearAlgebra 📕 Probability: https://imp.i384100.net/Probability OTHER RELATED COURSES (7 day free trial) 📕 ⭐ Deep Learning Specialization: https://imp.i384100.net/Deep-Learning 📕 Python for Everybod