Логистическая Регрессия | Logistic Regression | Линейная модель для классификации |МАШИННОЕ ОБУЧЕНИЕ

You can support the channel by subscribing to 🎉 https://boosty.to/machine_learrrning 🎉 https://vk.com/machine_learrrning 🎉 You can also donate coins to https://www.donationalerts.com/r/mach... TG channel https://t.me/machine_learrrning VK group https://vk.com/machine_learrrning I teach courses at an online university on programs related to AI and Data Science: https://go.redav.online/13d10800fd8342c0 (Netology) Come to my classes! Courses on the Stepik platform: 1. Python Libraries for Data Science https://stepik.org/a/129105 2. Introduction to Neural Networks (Keras/Tensorflow) https://stepik.org/a/127274 ❓ Questions we'll find answers to in this video: What is logistic regression How is logistic regression trained What is likelihood What is the logloss (cross-entropy) loss function 🌟 Linear Regression    • РЕАЛИЗАЦИЯ ЛИНЕЙНОЙ РЕГРЕССИИ | Линейная р...   0:00 Introduction 0:05 Lesson Plan 0:20 What you need to know for the lesson 0:49 Support https://boosty.to/machine_learrrning 0:49 Support https://vk.com/machine_learrrning 1:14 What is logistic regression 2:06 Linear Classification 4:11 Sigmoid for Linear Classification 6:02 Logistic Regression in Simple Terms 6:24 How Logistic Regression is Trained 6:43 Comparing Logistic Regressions 8:00 Introduction to Logloss 8:06 Likelihood 8:42 A Simple Example of Calculating Likelihood 10:51 Calculating Likelihood on All Data 12:34 Obtaining the Logistic Loss Function 12:41 Calculating Logloss on Likelihood 13:13 Comparing Cross-Entropy 13:38 Deriving Cross-Entropy from Likelihood 13:41 Likelihood Calculation Formula 14:08 An Example of Calculating Likelihood Using the Formula 15:40 Deriving the Logloss Formula 16:08 Maximum Likelihood Method 16:22 How to Obtain Logloss from Likelihood 17:34 Gradient Logloss 18:06 Logistic Regression Training Summary 18:40 Spoiler for the Next Video 18:54 Lesson Summary 19:01 Logistic Regression is Linear Regression with a Sigmoid 19:21 Likelihood 19:30 Logloss, Cross-entropy 19:42 ♡