https://github.com/hithesh111/Hith100

My 100 Days of ML Code Challenge Repository. Note: I stopped updating in the blogger site. The days after 100 days are documented in another repository.
https://github.com/hithesh111/Hith100

Last synced: 14 days ago
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My 100 Days of ML Code Challenge Repository. Note: I stopped updating in the blogger site. The days after 100 days are documented in another repository.

• Host: GitHub
• URL: https://github.com/hithesh111/Hith100
• Owner: hithesh111
• Created: 2019-08-16T04:12:18.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2022-02-08T14:39:00.000Z (almost 3 years ago)
• Last Synced: 2024-08-01T16:48:04.548Z (3 months ago)
• Language: Jupyter Notebook
• Homepage: https://github.com/hithesh111/HithBeyond100
• Size: 3.95 MB
• Stars: 103
• Watchers: 12
• Forks: 12
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
100 Days of ML (100% completed)

Dec 3, 2019 to 11 Mar, 2020


Beyond 100 Days Repository : https://github.com/hithesh111/HithBeyond100

Covered:



Machine Learning (Coursera course by Andrew NG)

Feature Engineering (How to Win a Data Science Competition course by National Research University Higher School of Economics)

Tree Based Models (Statquest Youtube Channel by Josh Starmer)

Data Competitions (Regression, Classification problems on Kaggle and Hackerearth)

Deep Learning (4 courses in the Deep Learning Specialization by deeplearning.ai)



 
Neural Networks and Deep Learning

 
Deep Neural Networks: Hyperparameter Tuning, Regularisation and Optimisation

 
Structuring Machine Learning Projects

 
Convolutional Neural Networks






Data Visualisation using Seaborn (Data Talks Youtube Channel)

Intro to Tensorflow for Deep Learning (Udacity course)

NLP (till Week 13 of Stanford NLP course by Jurafsky)



Languages, Tools and Libraries familiarised:



Python

Numpy

Pandas

Seaborn

Scikit-learn

NLTK



Note: All the resources used are available for free on the internet.

Highlights (Mini-projects, Implementations, Illustrations and Competition Submissions): 



 
K-Means Clustering (FIFA 19 Dataset) - Day 4

 
Anomaly Detection (Credit Card Transaction Fraud Detection) - Day 7

 
Kaggle House Price Prediction Competition - Day 18

 
Linear Regression from Scratch - Day 27

 
Logistic Regression from Scratch - Day 34

 
Polynomial Regression from Scratch - Day 36

 
HackerEarth Airplane Accident Severity Challenge - Days 39,62,63,67

 
Kaggle Titanic Disaster Survival Challenge - Day 40

 
Data Visualization using Seaborn Library - Days 44-53

 
Simple Neural Network using Tensorflow - Days 64

 
Image Augmentation and Classification - Day 71

 
Text Classification using NLTK - Day 81

 
Preprocessing, POS Tagging, Chunking - Day 96

 
Chat vs Article Text Classifier - Day 99
 



Day 1 - Linear Regression, Logistic Regression and Neural Networks.


3rd December

Revised Week 1 to Week 4 of Andrew Ng's Machine Learning course on Coursera which I had already completed a few months ago.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day001.ipynb

Day 2 - Backpropagation, Error Analysis, Bias and Variance


4th December

Revised Week 5 and Week 6 of Andrew Ng's Machine Learning course on Coursera which I had already completed a few months ago.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day002.ipynb

Day 3 - Support Vector Machines


5th December

Revised Week 7 of Andrew Ng's Machine Learning course on Coursera which I had already completed a few months ago.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day003.ipynb

Day 4 - K-Means Clustering (with FIFA 19 Dataset Project)


6th December

Revised Clustering from Andrew Ng's Machine Learning course on Coursera which I had already completed a few months ago and worked on FIFA 19 dataset to cluster a set of football players (using FIFA 19 in-game stats) into 4 classes expecting the clusters to reflect on the position,style and quality of play.

Project: https://github.com/hithesh111/Hith100/blob/master/fifa19playerclustering.ipynb


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day004.ipynb

Day 5 - Principal Component Analysis (PCA) 


7th December

Revised Dimensionality Reduction and Principal Component Analysis from Andrew Ng's Machine Learning course on Coursera which I had already completed a few months ago.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day005.ipynb

Day 6 - Anomaly Detection 


8th December

Revised Anomaly Detection from Andrew Ng's Machine Learning course on Coursera which I had already completed a few months ago and started working on Credit Card Transaction Dataset to detect fraudulent transactions.


More: https://hitheshai.blogspot.com/2019/12/100-days-challenge-day-6-anomaly-detection-credit-card-fraud-project.html

Day 7 - Anomaly Detection (Credit Card Fraud Transactions Project)


9th December

Completed the Credit Card Fraud Detection Project using Anomaly Detection algorithm.


Project: https://github.com/hithesh111/Hith100/blob/master/creditcardfraud.ipynb


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day007.ipynb

Day 8 - Recommender Systems 


10th December

Revised Recommender Systems from Week 9 of Andrew Ng's Machine Learning course on Coursera which I had already completed a few months ago.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day008.ipynb

Day 9 - Gradient Descent with Large Datasets, Online Learning, Photo OCR 


11th December

Revised Gradient Descent with Large Datasets, Online Learning, Photo OCR from last two weeks of Andrew Ng's Machine Learning course on Coursera which I had already completed a few months ago.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day009.ipynb

Day 10 -  Decision Trees and Random Forests


12th December

Learnt about Decision Trees and Random Forests from StatQuest Youtube channel


More: hhttps://github.com/hithesh111/Hith100/blob/master/100Days/day010.ipynb

Day 11 - Days 1 - 10 Review and Quizzes


13th December

Took quizzes on few topics covered in days 1-10, filled gaps in understanding certain concepts, cleared some doubts and found some new and related information.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day011.ipynb

Day 12 - Regression Trees


14th December

Learnt about Regression Trees from StatQuest YouTube Channel


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day012.ipynb

Day 13 - Gradient Boost


15th December

Learnt about Gradient Boosting from videos on Statquest Youtube channel 


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day013.ipynb

Day 14 -  Datasets and Feature Engineering


16th December

Learnt feature engineering methods from Krish Naik's Youtube channel and videos from How to Win a Data Science Competition Course on Youtube 


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day014.ipynb

Day 15 - Kaggle House Price Prediction Competition Part 1 


17th December

Entered House Price Prediction Competition on Kaggle and tried various methods of preprocessing the data and selecting features learnt yesterday. 


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day015.ipynb

Day 16 - Kaggle House Price Prediction Competition Part 2 


18th December

Found and created more meaningful features and tuned Random Forest thresholds. Best submission gave a MSE of log error value of 0.15600 and was ranked 3729/5775.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day016.ipynb

Day 17 - Kaggle House Price Prediction Competition Part 3 


19th December

Tried to tune the random forest and played around with the Random Forest parameters even more. Tried Gradient Boost. Made very slight progress in the score (0.15522)


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day017.ipynb

Day 18 - Kaggle House Price Prediction Competition Part 4 (Summary) 


20th December

Tried encoding various variables according to how they correlate with the SalePrice and played around with Linear Regression and GradientBoosting parameters. Made slight progress and jumped few steps on the leaderboard.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day018.ipynb


Code: https://github.com/hithesh111/Hith100/blob/master/house_price_competition_kaggle.ipynb 

Day 19 - San Franscisco Crime Classification Competition Part 1 


21st December

 Started working on Kaggle San Francisco Crime Classification competition.


More: hhttps://github.com/hithesh111/Hith100/blob/master/100Days/day019.ipynb

Day 20 - San Franscisco Crime Classification Competition Part 2 


22nd December

Tried to modify the data to create useful labels for the model. 


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day020.ipynb

Day 21 - Deep Learning Prerequisites 


23rd December

Skimmed through Part I (prerequisites for rest of the book) of Ian Goodfellow's Deep Learning book at deeplearningbook.org 


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day021.ipynb

Day 22 - Neural Networks and Deep Learning W1 


24th December

Watched videos of Week 1 of Neural Networks and Deep Learning by deeplearning.ai on Youtube 


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day022.ipynb

Day 23 - Neural Networks and Deep Learning W2 


25th December

Watched videos of Week 2 of Neural Networks and Deep Learning by deeplearning.ai on Youtube 


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day023.ipynb

Day 24 - Neural Networks and Deep Learning W3 


26th December

Watched videos of Week 2 and Week 3 of Neural Networks and Deep Learning by deeplearning.ai on Youtube 


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day024.ipynb

Day 25 - Neural Networks and Deep Learning W4 


27th December

Watched videos of Week 4 of Neural Networks and Deep Learning by deeplearning.ai on Youtube 


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day025.ipynb

Day 26 - Deep Neural Network Implementation 


28th December

Tried implementing a deep neural network with 4 layers to approximate complex functions added with normally distributed noise.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day026.ipynb

Day 27 - Linear Regression from Scratch


29th December

Implemented Linear Regression using Gradient Descent using only numpy matrix operations.


Code: https://github.com/hithesh111/Hith100/blob/master/100Days/day027.ipynb 


https://github.com/hithesh111/Hith100/blob/master/linear_regression_gradient_descent.ipynb

Day 28 - Regularization of Deep Neural Networks


30th December

Watched some videos from Week 1 of Hyperparameter Tuning, Regularization and Optimization course which is Part 2 of Coursera Deep Learning Specialization on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day028.ipynb

Day 29 - Exploding weights and Gradient Checking


31st December

Watched remaining videos from Week 1 of Hyperparameter Tuning, Regularization and Optimization course which is Part 2 of Coursera Deep Learning Specialization on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day029.ipynb

Day 30 -  Mini-Batch Gradient Descent, Exponentially Weighted Averages 


1st January 2020

Watched videos from Week 2 of Hyperparameters, Regularization and Optimization Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day030.ipynb

Day 31 - Tuning Process 


2nd January

Watched some videos from Week 3 of Hyperparameters, Regularization and Optimization Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day031.ipynb

Day 32 - Batch Normalization 


3rd January

Watched some videos from Week 3 of Hyperparameters, Regularization and Optimization Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day032.ipynb

Day 33 - Softmax Classifier 


4th January

Watched remaining videos from Week 3 of Hyperparameters, Regularization and Optimization Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day033.ipynb

Day 34 - Logistic Regression from Scratch 


5th January

Implemented Logisic Regression using only numpy matrix operations.


https://github.com/hithesh111/Hith100/blob/master/100Days/day034.ipynb

Code: https://github.com/hithesh111/Hith100/blob/master/Implementations/logistic_regression_from_scratch.ipynb 


Day 35 - Metrics and Train/Dev/Test Split 


6th January

Watched some videos from Week 1 of Structuring Machine Learning Projects Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day035.ipynb

Day 36 - Polynomial Regression from Scratch 


7th January

Implemented Polynomial Regression using only numpy matrix operations.


Code: https://github.com/hithesh111/Hith100/blob/master/100Days/day036.ipynb 


Day 37 - Human Level Performance and Bayes Error 


8th January

Watched remaining videos from Week 1 of Structuring Machine Learning Projects Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day037.ipynb

Day 38 - Error Analysis 


9th January

Watched videos from Week 2 of Structuring Machine Learning Projects Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day038.ipynb

Day 39 -  HackerEarth Airplane Accident Severity Challenge 


10th January

Participated in Airplane Accident Severity Classification Challenge on Hackerearth, made submission. Competition ends on 9th February. Currently 17th out of 520 on the leaderboard. Will upload the code once the competition is over.


Competition Details and More: https://hitheshai.blogspot.com/2020/01/100-days-of-ml-day-39-hackerearth-airplane-accident-severity-challenge.html

https://github.com/hithesh111/Hith100/blob/master/100Days/day039.ipynb

Day 40 -  Kaggle Titanic Disaster Survival Challenge 


11th January

Participated in Titanic Survival Classification Challenge on Kaggle. Currenly top 18% on the leaderboard.


Code: https://www.kaggle.com/hithesh111/kernel13c856e03f?scriptVersionId=26695158

https://github.com/hithesh111/Hith100/blob/master/100Days/day040.ipynb

Day 41 - Data Mismatch 


12th January

Watched videos from Week 2 of Structuring Machine Learning Projects Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day041.ipynb

Day 42 - Transfer Learning, Multitask Learning and End to End Learning 


13th January

Watched remaining videos from Week 2 of Structuring Machine Learning Projects Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day042.ipynb

Day 43 - AdaBoost 


14th January

Watched a video about AdaBoost Model Course on StatQuest Channel on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day043.ipynb

Day 44 - Seaborn: Distplots and KDE 


15th January

Watched a video about Distplots on Seaborn from Data Talks Youtube Channel and played around with important parameters.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day044.ipynb

Day 45 - Seaborn: kdeplots 


16th January

Watched a video about kdeplots on Seaborn from Data Talks Youtube Channel and played around with important parameters.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day045.ipynb

Day 46 - Seaborn: pairplots 


17th January

Watched a video about pairplots on Seaborn from Data Talks Youtube Channel and played around with important parameters.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day046.ipynb

Day 47 - Seaborn: stripplots and swarmplots 


18th January

Watched videos about stripplots and swarmplots on Seaborn from Data Talks Youtube Channel and played around with important parameters.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day047.ipynb

Day 48 - Seaborn: boxplots and jointplots 


19th January

Watched videos about boxplots and swarmplots on Seaborn from Data Talks Youtube Channel and played around with important parameters.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day048.ipynb

Day 49 - Seaborn: violinplots 


20th January

Watched a video about violinplots on Seaborn from Data Talks Youtube Channel and played around with important parameters.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day049.ipynb

Day 50 - Seaborn: lmplots 


21st January

Watched a video about lmplots on Seaborn from Data Talks Youtube Channel and played around with important parameters.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day050.ipynb

Day 51 - Seaborn: pointplots, barplots, countplots 


22nd January

Watched a video about pointplots,barplots and countplots on Seaborn from Data Talks Youtube Channel and played around with important parameters.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day051.ipynb

Day 52 - Seaborn: catplots,heatmaps  


23rd January

Watched videos about catplots,heatmaps on Seaborn from Data Talks Youtube Channel and played around with important parameters.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day052.ipynb

Day 53 - Seaborn: tsplots,boxenplots,facetgrid  


24th January

Watched videos about tsplots,boxenplots,facetgrid on Seaborn from Data Talks Youtube Channel and played around with important parameters.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day053.ipynb

Day 54 - Convolutions and Edge Detection 


25th January

Watched lectures from Week 1 of Convolutional Neural Networks Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day054.ipynb

Day 55 - Layers in CNN 


26th January

Watched lectures from Week 1 of Convolutional Neural Networks Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day055.ipynb

Day 56 - Classic CNN Architectures and ResNets 


27th January

Watched lectures from Week 2 of Convolutional Neural Networks Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day056.ipynb

Day 57 - Inception Networks, Transfer Learning, Data Augmentation


28th January

Watched lectures from Week 2 of Convolutional Neural Networks Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day057.ipynb

Day 58 - Object Localization and Landmark Detection


29th January

Watched lectures from Week 3 of Convolutional Neural Networks Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day058.ipynb

Day 59 - YOLO Algorithm


30th January

Watched lectures from Week 3 of Convolutional Neural Networks Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day059.ipynb

Day 60 - Face Recognition


31st January

Watched lectures from Week 4 of Convolutional Neural Networks Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day060.ipynb

Day 61 - Neural Style Transfer


1st February

Watched lectures from Week 4 of Convolutional Neural Networks Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day061.ipynb

Day 62 -  HackerEarth Airplane Accident Severity Challenge 


2nd February

Improved the score and rank in the Airplane Accident Severity Classification Challenge on Hackerearth, afer understanding the data better, doing better preprocessing and finding sweet spot of model parameters. Jumped from rank 330 (94.3 percentile) to 104 (98.2 percentile) on the leaderboard.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day062.ipynb

Day 63 -  HackerEarth Airplane Accident Severity Challenge 


3rd February

Since many features roughly follow normal distribution(observed using plots) I tried using multivariate normal pdf to predict which of the four severity does the accident most likely belong. But results were terrible even on the training set(42% accuracy). No improvements on the leaderboard.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day063.ipynb

Day 64 - Getting Started with Tensorflow 


4th February

Completed Lessons 1 and 2 of Intro to Tensorflow for Deep Learning Course on Udacity and coded a simple Neural Network for a Linear Function using tensorflow.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day064.ipynb

Day 65 - Dense Networks vs CNN for Image Classification


5th February

Completed Lessons 3 and 4 of Intro to Tensorflow for Deep Learning Course on Udacity which are about using Dense Networks and CNN for Image Classification Task.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day065.ipynb

Day 66 - Horses or Humans Image Classification


6th February

Worked on training a CNN for classifying humans and horses using Tensorflow.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day066.ipynb

Day 67 - HackerEarth Airplane Accident Severity Challenge Part 4


7th February

Built a Deep Neural Network to classify severity of the Airplane Accident. Accuracy is around 94% on the dev set and got a 0.84 score on the competition test set which is not an improvement on the Gradient Boosting Model.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day067.ipynb

Day 68 - HackerEarth Airplane Accident Severity Challenge Part 5


8th February

Tried using an ensemble of Deep Neural Networks to classify severity of the Airplane Accident


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day068.ipynb

Day 69 - CNN for Coloured Images


9th February

Halfway through Lessons 5 of Intro to Tensorflow for Deep Learning Course on Udacity.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day069.ipynb

Day 70 - CNN for Coloured Images


10th February

Did 2nd half of Lessons 5 of Intro to Tensorflow for Deep Learning Course on Udacity.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day070.ipynb

Day 71 -  Image Augmentation and Classification Exercise


11th February

Did Exercise of Lesson 5 of Intro to Tensorflow for Deep Learning Course on Udacity which is to classify flower images into 5 types.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day071.ipynb

Day 72 - Transfer Learning


12th February

Lessons 6 of Intro to Tensorflow for Deep Learning Course on Udacity.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day072.ipynb

Day 73 - Tensorflow - Saving and Loading Models


13th February

Lesson 7 and 9 of Intro to Tensorflow for Deep Learning Course on 

Udacity.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day073.ipynb

Day 74 - Regular Expressions, Tokenization and Stemming


14th February

Started learning NLP from Dan Jurafsky's NLP Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day074.ipynb

Day 75 - Minimum Edit Distance


15th February

Section 3 of Dan Jurafsky's NLP Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day075.ipynb

Day 76 - Language Modeling and NGrams


16th February

Section 4 of Dan Jurafsky's NLP Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day076.ipynb

Day 77 - Smoothing


17th February

Section 4 of Dan Jurafsky's NLP Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day077.ipynb

Day 78 - Spelling Correction


18th February

Section 5 of Dan Jurafsky's NLP Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day078.ipynb

Day 79 -  Text Classification and Naive Bayes 


19th February

Section 6 of Dan Jurafsky's NLP Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day079.ipynb

Day 80 -  Text Classification using NLTK 


20th February

Watched videos from Sentdex's NLP with NLTK Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day080.ipynb

Day 81 -  Text Classification using NLTK 2


21st February

Implemented Text Classification with help of Sentdex's NLP with NLTK Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day081.ipynb

Day 82 - Sentiment Analysis


22nd February

Section 7 of Dan Jurafsky's NLP Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day082.ipynb

Day 83 - Discriminative Models


23rd February

Section 8 of Dan Jurafsky's NLP Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day083.ipynb

Day 84 - Discriminative Models 2


24th February

Section 8 of Dan Jurafsky's NLP Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day084.ipynb

Day 85 - Named Entity Recognition


25th February

Section 9 of Dan Jurafsky's NLP Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day085.ipynb

Day 86 - Sequence Models


26th February

Section 9 of Dan Jurafsky's NLP Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day086.ipynb

Day 87 - Relation Extraction


27th February

Section 10 of Dan Jurafsky's NLP Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day087.ipynb

Day 88 - Relation Extraction 2


28th February

Section 10 of Dan Jurafsky's NLP Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day088.ipynb

Day 89 - Maximum Entropy Model


29th February

Section 11 of Dan Jurafsky's NLP Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day089.ipynb

Day 90 - Maximum Entropy Model 2


1st March

Section 11 of Dan Jurafsky's NLP Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day090.ipynb

Day 91 - Maximum Entropy Model 2


2nd March

Section 12 of Dan Jurafsky's NLP Course on Youtube


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day091.ipynb

Day 92 - Parsing


3rd March

Section 13 of Dan Jurafsky's NLP Course on Youtube and updated this readme to include a summary and upcoming learning plans.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day092.ipynb

Day 93 - Grammar Transfer


4th March

Section 15 of Dan Jurafsky's NLP Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day093.ipynb

Day 94 - CKY 


5th March

Section 15 of Dan Jurafsky's NLP Course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day094.ipynb

Day 95 -  Tokenizing, Stopwords and Stemming 


6th March

Watched lectures from sentdex's NLP with Python and NLTK course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day095.ipynb

Day 96 - Preprocessing, POS Tagging, Chunking


7th March

Implemented Preprocessing Methods like Tokenization, Stemming and Stopword Removal. Watched lectures from sentdex's NLP with Python and NLTK course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day096.ipynb

Day 97 - Named Entity Recognition, Lemmatization, NLTK Corpora


8th March

Watched lectures from sentdex's NLP with Python and NLTK course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day097.ipynb

Day 98- Wordnet


9th March

Watched lectures from sentdex's NLP with Python and NLTK course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day098.ipynb

Day 99- Chat vs Article Text Classifier


10th March

Built a text classifier (into chat and article) very similar to the one discussed in the article 'Naive Bayes Classifier for Text Classification' by Jaya Aiyappan (classifying sentences into questions and statements.)


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day099.ipynb

Day 100 - Saving Models, Scikit-Learn Incorporation


11th March

Watched lectures from sentdex's NLP with Python and NLTK course on Youtube.


More: https://github.com/hithesh111/Hith100/blob/master/100Days/day100.ipynb