100-Days-Of-ML-Code

100 Days of Machine Learning Coding as proposed by Siraj Raval

Get the datasets from here

Data PreProcessing | Day 1

Check out the code from here.

Simple Linear Regression | Day 2

Check out the code from here.

Multiple Linear Regression | Day 3

Check out the code from here.

Logistic Regression | Day 4

Logistic Regression | Day 5

Moving forward into #100DaysOfMLCode today I dived into the deeper depth of what actually Logistic Regression is and what is the math involved behind it. Learned how cost function is calculated and then how to apply gradient descent algorithm to cost function to minimize the error in prediction.
Due to less time I will now be posting a infographic on alternate days. Also if someone wants to help me out in documentaion of code and has already some experince in the field and knows Markdown for github please contact me on LinkedIn :) .

Implementing Logistic Regression | Day 6

Check out the Code here

K Nearest Neighbours | Day 7

Math Behind Logistic Regression | Day 8

#100DaysOfMLCode To clear my insights on logistic regression I was searching on the internet for some resource or article and I came across this article (https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bc) by Saishruthi Swaminathan.

It gives a detailed description of Logistic Regression. Do check it out.

Support Vector Machines | Day 9

Got an intution on what SVM is and how it is used to solve Classification problem.