Скачать или смотреть Machine Learning Tutorial Chap 7 | Part-1 Logistic Regression | Rohit Ghosh | GreyAtom

Welcome to the #DataScienceFridays Rohit Ghosh, a deep learning scientist and an Instructor at GreyAtom will take us through Logistic Regression in machine learning through this introduction series.

Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The outcome is measured with a dichotomous variable (i.e a variable that has only two values). In logistic regression, the dependent variable is binary or dichotomous, i.e. it only contains data coded as 1 (TRUE, success, pregnant, etc.) or 0 (FALSE, failure, non-pregnant, etc.). The goal of logistic regression is to find the best fitting (yet biologically reasonable) model to describe the relationship between the dichotomous characteristic of interest (dependent variable = response or outcome variable) and a set of independent (predictor or explanatory) variables. Logistic regression generates the coefficients (and its standard errors and significance levels) of a formula to predict a logit transformation of the probability of presence of the characteristic of interest.Rather than choosing parameters that minimize the sum of squared errors (like in ordinary regression), estimation in logistic regression chooses parameters that maximize the likelihood of observing the sample values.


This is the 1st in 6 videos about Logistic Regression in Machine Learning.

In this video, we will explore the Linear Regression in classification, why not?.

Logistic regression falls under the category of supervised learning; it measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic/sigmoid function. In spite of the name ‘logistic regression’, this is not used for regression problem where the task is to predict the real-valued output. It is a classification problem which is used to predict a binary outcome (1/0, -1/1, True/False) given a set of independent variables.Logistic regression is a bit similar to the linear regression or we can say it is a generalized linear model. In linear regression, we predict a real-valued output ‘y’ based on a weighted sum of input variables.The aim of linear regression is to estimate values for the model coefficients c, w1, w2,....,wn and fit the training data with minimal squared error and predict the output y.Logistic regression does the same thing, but with one addition. The logistic regression model computes a weighted sum of the input variables similar to the linear regression, but it runs the result through a special non-linear function, the logistic function or sigmoid function to produce the output y. Here, the output is binary or in the form of 0/1 or -1/1.

Complete Playlist for the Course: https://bit.ly/2Q1zvK6

After completing our 6-part Series on Logistic Regression in Machine Learning, you will be able to do the following:

Understand and apply the Logistic Regression algorithm
Understand Gradient Descent in Logistic Regression
Get in-depth knowledge about the various Evaluation Metrics
Learn how to tune hyperparameters using Grid Search and Random Search


Here’s the full syllabus of our 6-part video on Logistic Regression in Machine Learning:


Why Logistic over Linear Regression ?
Decision Boundary
Hyperparameter Tuning
Cost Function
Gradient Descent
Evaluating Matrix
Precision and Recall
Logarithmic Loss

#machinelearningtutorial #LogisticRegression #DataScience101 #Greyatom

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