| Day 1 | [Data Preproccessing](#1) | | Day 2 |[Simple Linear Regression](#2) | | Day 3 |[Multiple Linear Regression](#3) | | Day 4 |[Logistic Regression (pt1)](#4) |

| Day 5 | [Logistic Regression (pt2)](#1) | | Day 6 |[Logistic Regression Implementation](#6) | | Day 7 |[K Nearest Neighbours](#7) | | Day 8 | [Math Behind Logistic Regression](#8) |

| Day 9 | | | Day 10 | | | Day 11 | | | Day 12 | |

| Day 13 | | | Day 14 | | | Day 15 | | | Day 16 | |

| Day 17 | | | Day 18 | | | Day 19 | | | Day 20 | |

| Day 21 | | | Day 22 | | | Day 23 | | | Day 24 | |

| Day 25 | | | Day 26 | | | Day 27 | | | Day 28 | |

| Day 29 | | | Day 30 | | | Day 31 | | | Day 32 | |

| Day 9 | [Support Vector Machines](#9) | | Day 10 |[SVM & KNN](#10) | | Day 11 |[Implementation of K-NN](#11) | | Day 12 | [Support Vector Machines](#12) |

| Day 13 | [Naive Bayes Classifier](#13) | | Day 14 |[Implementation of SVM](#14) | | Day 15 |[Black Box Machine Learning](#15) | | Day 16 |[SVM using Kernel Trick](#16) |

| Day 17 | [](#17) | | Day 18 |[](#18) | | Day 19 |[](#19) | | Day 20 |[](#20) |

| Day 21 | [](#21) | | Day 22 |[](#22) | | Day 23 |[](#23) | | Day 24 |[](#24) |

| Day 25 | [](#25) | | Day 26 |[](#26) | | Day 27 |[](#27) | | Day 28 |[](#28) |

| Day 29 | [](#29) | | Day 30 |[](#30) | | Day 31 |[](#31) | | Day 32 |[](#32) |

| Day 33 | | | Day 34 | | | Day 35 | | | Day 36 | |

| Day 37 | | | Day 38 | | | Day 39 | | | Day 40 | |

| Day 41 | | | Day 42 | | | Day 43 | | | Day 44 | |

...

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@@ -88,84 +88,84 @@ Got an intution on what SVM is and how it is used to solve Classification proble

## SVM and KNN | Day 10 <a name="10"></a>

Learned more about how SVM works and implementing the K-NN algorithm.

## Implementation of K-NN | Day 11

## Implementation of K-NN | Day 11 <a name="11"></a>

Implemented the K-NN algorithm for classification. #100DaysOfMLCode

Support Vector Machine Infographic is halfway complete. Will update it tomorrow.

## Support Vector Machines | Day 12

## Support Vector Machines | Day 12 <a name="12"></a>

## Naive Bayes Classifier | Day 13 <a name="13"></a>

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

## Implementation of SVM | Day 14 <a name="14"></a>

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](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Code/Day%2013%20SVM.md).

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

## Naive Bayes Classifier and Black Box Machine Learning | Day 15 <a name="15"></a>

Learned about different types of naive bayes classifiers. Also started the lectures by [Bloomberg](https://bloomberg.github.io/foml/#home). First one in the playlist was Black Box Machine Learning. It gives the whole overview 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

## Implemented SVM using Kernel Trick | Day 16 <a name="16"></a>

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

## Started Deep learning Specialization on Coursera | Day 17 <a name="17"></a>

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

## Deep learning Specialization on Coursera | Day 18 <a name="18"></a>

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

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

## The Learning Problem , Professor Yaser Abu-Mostafa | Day 19 <a name="19"></a>

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

## Started Deep learning Specialization Course 2 | Day 20

## Started Deep learning Specialization Course 2 | Day 20 <a name="20"></a>

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

## Web Scraping | Day 21

## Web Scraping | Day 21 <a name="21"></a>

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

## Is Learning Feasible? | Day 22

## Is Learning Feasible? | Day 22 <a name="22"></a>

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

## Introduction To Statistical Learning Theory | Day 24

## Introduction To Statistical Learning Theory | Day 24 <a name="24"></a>

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

## Implementing Decision Trees | Day 25 <a name="25"></a>

Check the code [here.](https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Code/Day%2025%20Decision%20Tree.md)

## Jumped To Brush up Linear Algebra | Day 26

## Jumped To Brush up Linear Algebra | Day 26 <a name="26"></a>

Found an amazing [channel](https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw) 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.](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)

## Jumped To Brush up Linear Algebra | Day 27

## Jumped To Brush up Linear Algebra | Day 27 <a name="27"></a>

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.](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)

## Jumped To Brush up Linear Algebra | Day 28

## Jumped To Brush up Linear Algebra | Day 28 <a name="28"></a>

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.](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)

## Jumped To Brush up Linear Algebra | Day 29

## Jumped To Brush up Linear Algebra | Day 29 <a name="29"></a>

Completed the whole playlist today, videos 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.](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)

## Essence of calculus | Day 30

## Essence of calculus | Day 30 <a name="30"></a>

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.