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Day 7: 1) Studied the problem of overfitting and it's solution by regularization. 2) Exploring Neural networks and started third week's assignment. 3) Parallelly enjoying image processing with openCV.
#100DaysOfMLCodeHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Continued learning logistic regression and its vectorised implementation and also solved few problems by using advanced optimization algorithms. Also studied multiclass classification and one-vs-all classification algorithm.
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Day 5 1) Studied classification problems and learnt few algorithms to solve them. 2) Started learning logistic regression and its hypothesis representation. 3) Learnt the concept of decision boundary. ML is really fun! Enjoying this journey.
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Day 3 & 4 : 1. Learnt implementation of vectorization in multivariate linear regression. 2. Implemented few algorithms in Octave programming language.
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Day 2 Studied multivariable linear regression and learnt the normal equation method. Learnt polynomial regression and importance of feature scaling.
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Day 1 1. Studied briefly about different methods of machine learning. 2. Studied univariable linear regression model and learned gradient descent algorithm to solve any general hypothesis. 3. Played with different graphs in Octave programming language.
#100DaysOfMLCodepic.twitter.com/vNSxthYVnG
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