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

• 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)

• Python


• Numpy


• Pandas


• Seaborn


• Scikit-learn


• NLTK

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

• Sentiment Analysis


• Recommender Engine


• Chatbot


• Identifying Handwritten Letters in Regional Languages (including Data Augmentation)

• Support Vector Machines


• Principal Component Analysis


• General Adversarial Networks


• Image Style Transfer


• Question Answering


• Time Series Analysis

• R


• Tensorflow


• Keras


• Pytorch


• SQL

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-1-linear-regression-logistic-regression-neural-net.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-2-backpropagation-error-analysis-bias-and-variance.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-3-support-vector-machines.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-k-means-clustering-with-fifa-19-dataset-project.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-5-dimensionality-reduction-and-principal-component-analysis.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-7-anomaly-detection-credit-card-fraud-detection-project.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-8-recommender-systems.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-9-gradient-descent-with-large-datasets-online-learning-photo-ocr.html

Day 10 - Decision Trees and Random Forests
12th December
Learnt about Decision Trees and Random Forests from StatQuest Youtube channel
More: https://hitheshai.blogspot.com/2019/12/100-days-challenge-day-10-decision-trees-and-random-forests.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-11-days-1-10-review-and-quizzes.html

Day 12 - Regression Trees
14th December
Learnt about Regression Trees from StatQuest YouTube Channel
More: https://hitheshai.blogspot.com/2019/12/100-days-challenge-day-12-regression.html

Day 13 - Gradient Boost
15th December
Learnt about Gradient Boosting from videos on Statquest Youtube channel
More: https://hitheshai.blogspot.com/2019/12/100-days-challenge-day-13-gradient-boost.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-14-datasets-and-feature-engineering.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-15-kaggle-house-price-prediction-competition-part1.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-16-kaggle-house-price-prediction-competition-part2.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-17-kaggle-house-price-prediction-competition-part-3.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-18-kaggle-house-price-prediction-competition-part-4-summary.html
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: https://hitheshai.blogspot.com/2019/12/100-days-challenge-day-19-san-francisco-crime-classification-competition-part-1.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-20-san-francisco-crime-classification-competition-part-2.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-21-deep-learning-prerequisites.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-22-neural-networks-and-deep-learning-week-1.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-23-neural-networks-and-deep-learning-week-2.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-24-neural-networks-and-deep-learning-week-3.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-25-neural-networks-and-deep-learning-week-4.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-24-neural-networks-and-deep-learning-week-3.html

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/Implementations/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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-28-regularization-of-deep-neural-networks.html

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://hitheshai.blogspot.com/2019/12/100-days-challenge-day-29-exploding-weights-and-gradient-checking.html

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://hitheshai.blogspot.com/2020/01/100-days-challenge-day-30-mini-batch-gradient-descent-exponentially-weighted-averages.html

Day 31 - Tuning Process
2nd January
Watched some videos from Week 3 of Hyperparameters, Regularization and Optimization Course on Youtube.
More: https://hitheshai.blogspot.com/2020/01/100-days-challenge-day-31-tuning-process.html

Day 32 - Batch Normalization
3rd January
Watched some videos from Week 3 of Hyperparameters, Regularization and Optimization Course on Youtube.
More: https://hitheshai.blogspot.com/2020/01/100-days-challenge-day-32-batch-normalization.html

Day 33 - Softmax Classifier
4th January
Watched remaining videos from Week 3 of Hyperparameters, Regularization and Optimization Course on Youtube.
More: https://hitheshai.blogspot.com/2020/01/100-days-challenge-day-33-softmax-classifier.html

Day 34 - Logistic Regression from Scratch
5th January
Implemented Logisic Regression using only numpy matrix operations.
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

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

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