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https://github.com/harunurrashid97/100-Days-Of-ML-Code

A day to day plan for this challenge. Covers both theoritical and practical aspects
https://github.com/harunurrashid97/100-Days-Of-ML-Code

100-days-of-code 100daysofmlcode article data-preprocessing data-science datascience decision-tree eda exploratory-data-analysis implementation infographics linear-regression machine-learning machine-learning-algorithms python regression-algorithms siraj-raval-challenge textsummarization tutorials vizualization

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A day to day plan for this challenge. Covers both theoritical and practical aspects

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# 100 Days Of ML Code

![100 Days of ML Code challenge](InfoGraphs/GithubBanner.png)

:point_up: This Banner Design by my buddy [Sheikh Anikul Islam Hani](https://github.com/SheikhAnikulIslam):octocat:.He is awesome person.:ok_hand: :bust_in_silhouette:

The creation of this repository was inspired by [Siraj Raval's challenge](https://www.linkedin.com/feed/update/urn:li:activity:6420525903968825344/) to code machine learning for at least an hour everyday for 100 days.
I nervously accepted this challenge in addition to working full time in the 2018 summer semester. I will use this repository to store code, jupyter notebook examples, and thought processes.

You can follow me on [@Medium](https://medium.com/@harunurrashid97)for interesting blog articles.
## Day 0 : Aug 5 to 6 , 2018 | Challenge Starting peparation

**Today's Progress** : I have setup all the things I needed to complete this challenge and also completed chosing the projects I will work on .

**Thoughts** : Hope this will be exiciting ,will help me in learning Machine Learning in a more effective way .

## Day 1 : Aug 7 , 2018 | [Crash course of Data Science and ML with Python](https://www.linkedin.com/feed/update/urn:li:activity:6432532718810169344/)

**Today's Progress** : I have completed article and code for Crash course of Data Science and ML with Python.

**Thoughts** : It is challenging for Beginners.But i will try easily explain of all things for Crash course of Data Science and ML with Python.

**Link of Work**: [Article](https://hackernoon.com/10-ways-to-make-python-a-dangerous-language-for-data-science-6b88566ac040) => [code](http://bit.ly/2KC7EvY)

## Day 2 : Aug 8 , 2018 | [Exploratory Data Analysis(EDA)](https://www.linkedin.com/feed/update/urn:li:activity:6432869901274312704)

**Today's Progress** : Today I've spent many hours exploring Exploratory data analysis.It plays a role in the work of data science and machine learning.

**Thoughts** : I am giving a brief intro of Exploratory data analysis(EDA) in Python with help of pandas and matplotlib.And I am using a dataset on Super Store's for this.

**Link of Work**: [Article](https://hackernoon.com/overview-of-exploratory-data-analysis-with-python-6213e105b00b) => [code](http://bit.ly/2KytuAo)

## Day 3 : Aug 9 , 2018 | [Data PreProcessing](https://www.linkedin.com/feed/update/urn:li:activity:6433216669149159424)

![Data Prepocessing](InfoGraphs/Day3.png)

**Today's Progress** : Today I've spent many hours exploring Data Preprocessing.Check Out :point_up: this Amazing Infographic I made which gives a complete step by step guide to data preprocessing.

**Link of Work**: [Article](https://medium.com/datadriveninvestor/data-preprocessing-for-machine-learning-188e9eef1d2c) => [Code](http://bit.ly/2KDkTfT)

## Day 4 : Aug 12 , 2018 | [Simple Linear Regression](https://www.linkedin.com/feed/update/urn:li:activity:6434122254916902912)

![Simple Linear Regression](InfoGraphs/Day4.png)

**Today's Progress** : Today I've spent many hours exploring on Simple Linear Regression, check it out. I think infographics are a worthy replacement of lengthy articles on these topics.

**Link of Work**: [Article](http://bit.ly/2MbIzO4) => [Code](http://bit.ly/2MjvQrS)

## Day 5 : Sep 29 , 2018 | [Decision Trees](https://www.linkedin.com/feed/update/urn:li:activity:6451684610320502784)

![Decision Trees](InfoGraphs/Day5.png)

**Today's Progress** : Today I've spent many hours exploring on Decision tree classification algorithm. I think infographics are a worthy replacement of lengthy articles on these topics.

**Link of Work**: [Article](https://bit.ly/2jnSH5w) => [code](https://bit.ly/2xLec7M)

## Day 6 : Jan 2 , 2019 | [Summarization With Wine Reviews Using spaCy](https://twitter.com/Shimanto47/status/1080707882197278722)

![Text Summarization](https://i.imgur.com/DRBrg5m.gif)

**Today's Progress** : Today I've spent many hours exploring on Text summarization on NLP.

**Link of Work**: [Article](https://goo.gl/Gr7kcG) => [code](https://goo.gl/rZatDk)

## Day 7 : Jan 11 , 2019 | [Artificial Neural Networks (ANN)](https://twitter.com/Shimanto47/status/1083786783911890944)

![ANN](InfoGraphs/ANN-Day7.jpg)

**Today's Progress** : Today I've spent many hours exploring on Artificial Neural Networks (ANN).

**Link of Work**: [Article](https://goo.gl/E2mTx1) => [code](https://goo.gl/xSJZCZ)

## Day 8 : Jan 17 , 2019 | [Naive Bayes](https://www.linkedin.com/feed/update/urn:li:activity:6491368088909504512)



**Today's Progress** : Today I've spent many hours exploring on Naive Bayes (NB).

**Link of Work**: [Article](https://goo.gl/YhgWKm) => [code](https://goo.gl/2KjZkC)

## Day 9 : Jan 20 , 2019 | [Twitter Sentimental Analysis](https://www.linkedin.com/feed/update/urn:li:activity:6492808074607333376)



**Today's Progress** : Today I've spent many hours exploring on Twitter Sentimental Analysis(TSA).

**Link of Work**: [Article](https://goo.gl/tn4t8E) => [code](https://goo.gl/JwQQ8g)

## Day 10 : Jan 22 , 2019 | [Support Vector Machine](https://www.linkedin.com/feed/update/urn:li:activity:6493492920488984576)



**Today's Progress** : Today I've spent many hours exploring on Support Vector Machine(SVM).

**Link of Work**: [Article](https://goo.gl/gKvFKk) => [code](https://goo.gl/vjSCwP)

## Day 11-13 : Feb 2,4,7 , 2019 | [A 3 day Python Bootcamp for ML Beginners](https://www.linkedin.com/feed/update/urn:li:activity:6498961828020916224)



**Today's Progress** : A simple effort from me to make a Python bootcamp 3 Days for beginners who are enthusiastic about Machine Learning.

**Link of Work**: [Article](https://goo.gl/fiyAzC) => [code](https://goo.gl/iF59Kw)

# Contributing

[![](https://sourcerer.io/fame/harunshimanto/harunshimanto/100-Days-Of-ML-Code/images/0)](https://sourcerer.io/fame/harunshimanto/harunshimanto/100-Days-Of-ML-Code/links/0)[![](https://sourcerer.io/fame/harunshimanto/harunshimanto/100-Days-Of-ML-Code/images/1)](https://sourcerer.io/fame/harunshimanto/harunshimanto/100-Days-Of-ML-Code/links/1)[![](https://sourcerer.io/fame/harunshimanto/harunshimanto/100-Days-Of-ML-Code/images/2)](https://sourcerer.io/fame/harunshimanto/harunshimanto/100-Days-Of-ML-Code/links/2)[![](https://sourcerer.io/fame/harunshimanto/harunshimanto/100-Days-Of-ML-Code/images/3)](https://sourcerer.io/fame/harunshimanto/harunshimanto/100-Days-Of-ML-Code/links/3)[![](https://sourcerer.io/fame/harunshimanto/harunshimanto/100-Days-Of-ML-Code/images/4)](https://sourcerer.io/fame/harunshimanto/harunshimanto/100-Days-Of-ML-Code/links/4)[![](https://sourcerer.io/fame/harunshimanto/harunshimanto/100-Days-Of-ML-Code/images/5)](https://sourcerer.io/fame/harunshimanto/harunshimanto/100-Days-Of-ML-Code/links/5)[![](https://sourcerer.io/fame/harunshimanto/harunshimanto/100-Days-Of-ML-Code/images/6)](https://sourcerer.io/fame/harunshimanto/harunshimanto/100-Days-Of-ML-Code/links/6)[![](https://sourcerer.io/fame/harunshimanto/harunshimanto/100-Days-Of-ML-Code/images/7)](https://sourcerer.io/fame/harunshimanto/harunshimanto/100-Days-Of-ML-Code/links/7)