Logistic Regression for Binary Classification

Gradient Descent:
• Supervised Learning : ...
Maximum Likelihood Estimation:
• Maximum Likelihood Est...
Naive Bayes:
• Naive Bayes : A simple...
Linear Regression:
• Linear Regression : No...
Python Codes:
towardsdatasci...
www.stat.cmu.e...
Logistic Regression (Handwritten Digit Recognition):
realpython.com...
Logistic Regression (University admission):
towardsdatasci...
Logistic Regression (Consumer Purchase):
www.geeksforge...
Logistic Regression (Multi-Class):
• Logistic Regression fo...
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[@HrichaAcharya]

Sir, at time 17:35 in the video how do we arrive at the relation between "\theta" (model parameters) and function h?

[@EvolutionaryIntelligence]

Yes, thats right! :)
So, in other words, we are explicitly designing our hypothesis function so that it satisfies the stated condition.

[@VijayChakravarty-pz4qv]

Is Theta0 the first entry in the vector Theta. As in Theta=( Theta0, Theta1,...)?

[@EvolutionaryIntelligence]

That depends on how theta and the input vector are defined. If theta_0 is included in theta vector, then the input vector needs to be augmented with a unit element (value=1). Its just a matter of convention, but one has to be careful while implementing the code from scratch.

[@ShivanBhatt-sx5ib]

Sir, are there any advantages of expressing probability as log odds or log odds of hypothesis being a linear function?

[@EvolutionaryIntelligence]

Certainly yes! Firstly, linear functions are easier to interpret. Secondly, linear functions and their gradients are easier to compute.