Machine Learning Tutorial Chap 7 | Part-3 Logistic Regression | Rohit Ghosh | GreyAtom
Channel: GreyAtom EduTech
Date: 05/07/2019

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 3rd in 6 videos about Logistic Regression in Machine Learning. 

In this video, we will explore the  Choice of loss function and cost function. 


In mathematical optimization and decision theory, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function. An objective function is either a loss function or its negative (in specific domains, variously called a reward function, a profit function, a utility function, a fitness function, etc.), in which case it is to be maximized.


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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