# 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.

## 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

## 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.

## Implementation of SVM | Day 14

Today I implemented SVM on linearly related data. Used Scikit-Learn library. In scikit-learn we have SVC classifier which we use to achieve this task. Will be using kernel-trick on next implementation. Check the code here.

## Naive Bayes Classifier and Black Box Machine Learning | Day 15

Learned about diffrent types of naive bayes classifer also started the lectures by Bloomberg. first one in the playlist was Black Box Machine Learning. It gave the whole over view about prediction functions, feature extraction, learning algorithms, performance evaluation, cross-validation, sample bias, nonstationarity, overfitting, and hyperparameter tuning.

## Implemented SVM using Kernel Trick | Day 16

Using Scikit-Learn library implemented SVM algorithm along with kernel function which maps our data points into higher dimension to find optimal hyperplane.

## Started Deep learning Specialization on Coursera | Day 17

Completed the whole Week 1 and Week 2 on a single day. Learned Logistic regression as Neural Network.

## Deep learning Specialization on Coursera | Day 18

Completed the Course 1 of the deep learning specialization. Implemented a neural net in python.

## The Learning Problem , Professor Yaser Abu-Mostafa | Day 19

Started Lecture 1 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. It was basically an intoduction to the upcoming lectures. He also explained Perceptron Algorithm.

## Started Deep learning Specialization Course 2 | Day 20

Completed the Week 1 of Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization.

## Web Scraping | Day 21

Watched some tutorials on how to do web scaping using Beautiful Soup in order to collect data for building a model.

## Is Learning Feasible? | Day 22

Lecture 2 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. Learned about Hoeffding Inequality.

## Decision Trees | Day 23

## Introduction To Statistical Learning Theory | Day 24

Lec 3 of Bloomberg ML course introduced some of the core concepts like input space, action space, outcome space, prediction functions, loss functions, and hypothesis spaces.

## Implementing Decision Trees | Day 25

Check the code here.

## Jumped To Brush up Linear Algebra | Day 26

Found an amazing channel on youtube 3Blue1Brown. It has a playlist called Essence of Linear Algebra. Started off by completing 4 videos which gave a complete overview of Vectors, Linear Combinations, Spans, Basis Vectors, Linear Transformations and Matrix Multiplication.

Link to the Playlist here.

## Jumped To Brush up Linear Algebra | Day 27

Continuing with the playlist completed Next 4 Videos discussing topics 3D Transformations, Determinants, Inverse Matrix, Column Space, Null Space and Non-Square Matrices.

Link to the Playlist here.

## Jumped To Brush up Linear Algebra | Day 28

In the playlist of 3Blue1Brown completed another 3 Videos from the essence of linear algebra. Topics covered were Dot Product and Cross Product.

Link to the Playlist here.

## Jumped To Brush up Linear Algebra | Day 29

Completed the whole Playlist today, Videos from 12 - 14. Really an amazing playlist to refresh the concepts of Linear Algebra. Topics covered were the change of basis, Eigenvectors and Eigenvalues, and Abstract Vector Spaces.

Link to the Playlist here.

## Essence of calculus | Day 30

Completing the playlist - Essence of Linear Algebra by 3blue1brown a suggestion popped up by youtube regarding a series of videos again by the same channel 3Blue1Brown. Being already Impressed by the previous series on Linear algebra I dived straight into it. Completed about 5 videos on topics such as Derivatives, Chain Rule, Product Rule, and derivative of exponential.

Link to the Playlist here.

## Essence of calculus | Day 31

Watched 2 Videos on topic Implicit Diffrentiation and Limits from the playlist Essence of Calculus.

Link to the Playlist here.

## Essence of calculus | Day 32

Watched the remaining 4 videos covering topics Like Integration and Higher order derivatives.

Link to the Playlist here.

## Random Forests | Day 33

## Implementing Random Forests | Day 34

Check the code here.