# #100DaysofMLCode Challenge Log

## Day 1
* Definition of Machine Learning
* Application of Machine Learning
* Installation of Anaconda Python
* Installation of R and RStudio IDE

## Day 2
* Basics of Data pre processing
* Importing libraries in Python
* Importing Data Sets in R and Python

## Day 3
* Data Pre-processing: Handling missing Data in Python and R

## Day 4
* Data Pre-processing: Encoding categorical data in Python and R

## Day 5
* Write up for entire Data-preprocessing till now

## Day 6
* Splitting Data set into train data and test data
* Feature Scaling

## Day 7
* Writeups for Splitting Data Sets and Feature Scaling
* Wrapping up Data-Preprocessing part

## Day 8
* Introduction to Simple Linear Introduction
* Simple Linear Regression Code in Python

## Day 9
* Simple Linear Regression Code in R Programming

## Day 10
* Write up for Simple Linear Regression with explanation of coding in both R and Python
* Introduction to Multi Linear Regression

## Day 11
* Multi Linear Regression code in Python with writup

## Day 12
* Multi Linear Regression code in R Programming

## Day 13
* Multi Linear Regression conclusion and writeup for both R and Python programming

## Day 14
* Introduction to Polynomial Regression

## Day 15
* Stepwise Polynomial Regression Code in Python and its writeup

## Day 16
* Stepwise Polynomial Regression Code in R programming and its writeup

## Day 17
* Introduction to Support Vector Regression 

## Day 18
* Support Vector Regression Code in Python

## Day 19
* Support Vector Regression Code in R Programming

## Day 20
* Introduction to Decision Tree Regression
* Decision Tree Regression in Python

## Day 21
* Decision Tree Regression in R Programming

## Day 22
* Introduction to Random Forest Regression
* Random Forest Regression in Python

## Day 23
* Random Forest Regression In R

## Day 24
* Concluding Regression 
* Comparing Different Regression Models
* R Squared Approach
* Adjusted R Squared Approach

## Day 25
* Introduction to Classifications
* Introduction to Logistic Regression

## Day 26
* Logistic Regression in Python

## Day 27
* Logistic Regression in R

## Day 28
* Introduction To K-Nearest Neighbor Classification

## Day 29
* Classification of data set using K-Nearest Neigbors in Python

## Day 30
* Classification of data set using K-Nearest Neigbors in R

## Day 31
* Introduction to classification using Support Vector Machines.

## Day 32
* Support Vector Machine Classification in Python

## Day 33
* Support Vector Machine Classification in R Programming

## Day 34-37

> These days are clubbed together because I had to spend full time in travel and industrial visit at Xebia Gurugram, India office.

* Introduction to Kernel SVM and programmed them in Python and R

## Day 38
* Introduction to Classification using Naive Bayes Algorithm.

## Day 39
* Data Classification using Naive Bayes method in Python

## Day 40
* Data Classfication using Naive Bayes method in R

## Day 41
* Introduction to Decision Tree Classification

## Day 42
* Decision Tree Classification in Python

## Day 43
* Decision Tree Classification in R

## Day 44
* Introduction to Random Forest Classification

## Day 45
* Random Forest Classification in Python

## Day 46
* Random Forest Classification in R
* Wrapping up Data classification

## Day 47
* Introduction to Clustering Algorithms
* Introduction to K-Means clustering

## Day 48
* K-Means clustering in Python

## Day 49
* K-Means clustering in R

## Day 50
* Introduction to Hierarchical Clustering

## Day 51
* Hierarchical Clustering in Python

## Day 52
* Hierarichal Clustering in R

## Day 53
* Comparison between different clustering algorithms

## Day 54
* Introduction to Association Rule Learning

## Day 55
* Apriori Rule in R

## Day 56
* Introduction to Apriori Rule in Python and template file

## Day 57
* Apriori Rule in Python

## Day 58
* Association Rule Learning with Eclat

## Day 59
* Introduction to Reinforcement Learning
* Introduction to Upper Confidence Bounds Algorithm

## Day 60
* Reinforcement Learning using Random Selections in both R and Python

## Day 61
* Upper Confidence Bound algorithms in R
* Upper Confidence Bound algorithms in Python

## Day 62
* Introduction to Thompson Sampling 

## Day 63
* Thompson Sampling in Python

## Day 64
* Thompson Sampling in R

## Day 65
* Introduction to Natural Language Processing

## Day 66
* NLP programming in Python

## Day 67
* NLP programming in R

## Day 68
* Introduction to Deep Learning
* Introduction to Artificial Neural Networks

## Day 69
* Artificial Neural Networks in Python

## Day 70
* Artificial Neural Networks in R

## Day 71
* Introduction to Convolutional Neural Networks

## Day 72
* Convolutional Neural Networks in Python

## Day 73
* Introduction to Dimensionality Reduction
* Introduction to Principal Component Analysis (PCA)

## Day 74
* Principal Component Analysis in Python

## Day 75
* Principal Component Analysis in R

## Day 76
* Introduction to Linear Discriminant Analysis

## Day 77
* Linear Discriminant Analysis in Python

## Day 78
* Linear Discriminant Analysis in R

## Day 79
* Introduction to Kernel PCA

## Day 80
* Kernel PCA in Python

## Day 81
* Kernel PCA in R

## Day 82
* Introduction to Model Selection

## Day 83
* Introduction to Grid Selection and programming it in Python

## Day 84
* Grid Selection programmin in R

## Day 85
* K-fold Cross Validation in Python

## Day 86
* K-fold Cross Validation in R

## Day 87
* Introduction to XGBoost

## Day 88
* XGBoost programming in Python

## Day 89
* XGBoost programming in R

## Day 90
* Introduction to AIOps

## Day 91
* AIOps with MoogSoft

## Day 92
* AIOps with DataDog

## Day 93
* Data Visualization | Tableau

## Day 94
* Data Visulization | Power BI

## Day 95
* Data Visualization | Grafana

## Day 96
* Natural Language Processing | AWS Comprehend

## Day 97
* AWS Comprehend | Day 2

## Day 98-99
* Implementing Real Time Social Media mining project | Sentimental Analysis

## Day 100
* Starting my journey to Machine Learning on cloud. Follow my project [here](https://github.com/stormsinbrewing/Real_Time_Social_Media_Mining). 
