hithesh111/Hith100
My 100 Days of ML Code Challenge Repository.
| repo name | hithesh111/Hith100 |
| repo link | https://github.com/hithesh111/Hith100 |
| homepage | https://hitheshai.blogspot.com |
| language | Jupyter Notebook |
| size (curr.) | 4095 kB |
| stars (curr.) | 42 |
| created | 2019-08-16 |
| license | |
Beyond 100 Days Repository : https://github.com/hithesh111/HithBeyond100
Note: All the resources used are available for free on the internet.
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
