Repository containing all the major applications of Machine Learning / Deep Learning Projects done by me . Every project is sorted by category and followed by a small description of its application features, dataset and methods used.
Named entity Recognition model to extract informationand classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.
Link : Click Here
Using statistical modeling techniques for discovering the abstract “topics” that occur in a collection of documents and thus helping in automatic (unsupervised ) title / classification process. Here we used News Headlines from reputable Australian news source ABC (Australian Broadcasting Corporation) and used LDA, Genism doc2bow for modelling purposes.
Link : Click Here
Using different State-of-the-art techniques / models for natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.
i. Determing sentiment polarity for time-series modelling. Using sentiment analysis as a factor and determing how Stocks / demands are affected from people's tweets over twitter with time. Libraries used : NLTK, Flair
Link : Click Here
ii. Classifying IMDB reviews ( NLP )
Classifying IMDB reviews as positive or Negative based on the review text using LSTMs ( 92.4 % accuracy ) using fast.ai models ( AWD-LSTM ). Based on transfer learning approach with classificaiton model trained on Wikitext-103 dataset
Link : Click Here
iii. Airline tweet sentiment Analysis ( using Flair ) : Classifying airline passengers tweets for determing their in-flight experience using NLP
Link : Click Here
iv. NLTK sentiment Analysis : Click Here
v. TextBlob sentiment Analysis : Click Here
Using SOTA transformers and hugging face model to perform zero-shot classification or doing unsupervised modelling for text classification. Here determing product categories from unstructured dataset.
Link : Click here
Modelling resume and job description similarity for determining relavancy score and how close resume handles what's required in job. Simple model using cosine similarity ( to be continued )
Link : Click here
The program can detect hand from live video and through OpenCV and Convell Hull technique counts the number of fingers shown to camera.
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- YOLO Application
The program can automatically detect the Russian car number plate present in the image and Apply blurring to the image.
Link : Click Here
Classfying the MNIST Fashion dataset ( 60000 Images ) into different categories by CNN model in Keras.
Link : Click here
Applying basic image processing techniques such as : Sobel Edge detection, Thresholding, kernels & blending using OpenCV
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Using state of the art Tesseract OCR for extracting data from images.
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Using the feed parser library to extract RSS feeds for different podcasts and inserting the data into database into PostGRE's database. Hence automating the feed parsing for simple use in apps / website feeds
Link : Click Here
Time series modelling for APPLE stocks. Using past data to predict the future price of APPLE stocks using LSTM models.
Link : Click Here
Using data science / machine learning models for prediction of the net profit attributed to the entire future relationship with a customer (Customer Life time Value).
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Using Google's SOTA speech recognition library to automatically convert speect into text
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Classifying IMDB reviews as positive or Negative based on the review text using LSTMs ( 92.4 % accuracy ) using fast.ai models ( AWD-LSTM ).
Link : Click Here
Using collaborative filtering to sort out the Top movies from the MovieLens dataset ( containing 100k data )
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Using fastai vision to segment the images using ResNet34 CNN model ( Accuracy : 92.5% ) on Camvid Data
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Using fastai to find the center of the face in a head pose using ResNet34 CNN model
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Script based extraction of data from google images & Application of ResNet34 & ResNet50 CNN model to classify the images in custom bears dataset using fastai.
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Using BeautifulSoup to scrape data from Biggest German Real estate site and storing it in remote SQL database for analytics purposes.
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Using BeautifulSoup to scrape Food.com ( Food.com is a digital brand and online social networking service featuring recipes from home cooks and celebrity chefs, food news, new and classic shows, and pop culture )
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Extracting cars inventory data from Porsche.com.
Link : Click here
Using bs4 for scraping softwares data from Capterra website
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Scraping script for extracting data of all clinics from the portal
Link : Click Here