README.md

# 100-Days-Of-ML-Code

100 Days of Machine Learning Coding as proposed by [Siraj Raval](https://github.com/llSourcell)

Get the datasets from [here](https://github.com/Avik-Jain/100-Days-Of-ML-Code/tree/master/datasets)

## Data PreProcessing | Day 1
Check out the code from [here](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Code/Day%201_Data%20PreProcessing.md).

<p align="center">
  <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%201.jpg">
</p>

## Simple Linear Regression | Day 2
Check out the code from [here](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Code/Day2_Simple_Linear_Regression.md).

<p align="center">
  <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%202.jpg">
</p>

## Multiple Linear Regression | Day 3
Check out the code from [here](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Code/Day2_Multiple_Linear_Regression.md).

<p align="center">
  <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%203.jpg">
</p>

## Logistic Regression | Day 4

<p align="center">
  <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%204.jpg">
</p>

## 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](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Code/Day%206%20Logistic%20Regression.md)

## K Nearest Neighbours | Day 7
<p align="center">
  <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%207.jpg">
</p>

## 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.

## SVM and KNN | Day 10
Learned more about how SVM works and implementing the knn algorithm.

## Implementation of K-NN | Day 11  

Implemented the K-NN algorithm for classification. #100DaysOfMLCode 
Support Vector Machine Infographic is halfway complete will update it tomorrow.

## Support Vector Machines | Day 12
<p align="center">
  <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%2012.jpg">
</p>

## Naive Bayes Classifier | Day 13

Continuing with #100DaysOfMLCode today I went through the Naive Bayes classifier.
I am also implementing the SVM in python using scikit-learn. Will update the code soon.