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Name: Surendra Kumar Shringi
Type: User
Location: Bengalore, India
Name: Surendra Kumar Shringi
Type: User
Location: Bengalore, India
Microsoft Azure SDK for Python
This ChatBot is based on Python with NLTK. Its a basic chatbot.
Various Dockerfiles for Windows Containers
My_Site
Language-Detection-From-Text---Bi-gram-based It uses Bi-gram language model and bi-gram frequency addition classifier for language identification task. Trained over 6 languages namely German, English, Spanish, French, Italian and Dutch. The original source of the text corpus is wortschatz leipzig corpora. Both the train and test corpus were taken from this corpora. The training corpus consists of 30000 sentences from news/web domain. Test corpus 10000 unseen sentences from news/web domain. Also, the chosen six languages were such that the same languages are present in the LIGA twitter dataset which consists of 9066 tweets. Note : Directory path used for train and test corpus in code language-test.py, language-train.py and liga_test.py needs to be properly set accordingly.
Mail-Spam-Filtering It uses machine learning models to predict whether the email is spam or ligitimate. Best thing would be to follow my blog-post for implementation. The description about the steps to build a spam filter from scratch can be read from my blog: https://appliedmachinelearning.wordpress.com/2017/01/23/nlp-blog-post/ It is a python implementation using Naive Bayes Classifier and Support Vector Machines from Scikit-learn ML library. The results has been shown on two publicly open corpus. Ling-spam corpus Euron-spam corpus The link for corpus/dataset download is given in blog-post. Note : Directory path used for training and testing models in lingspam_filter.py and euron-spamfilter.py needs to be properly set accordingly.
In this blog-post, we will demonstrate how to achieve 90% accuracy in object recognition task on CIFAR-10 dataset with help of following concepts: 1. Deep Network Architecture 2. Data Augmentation 3. Regularization
It uses 2-layered fully connected/Dense Neural network model to predict whether the hotel reviews at TripAdvisor site are positive sentiment or negative sentiment. It is a python implementation utilizing Keras library for DNN. This problem statement came from a HackerEarth challenge: "Predict the Happiness" The accuracy score achieved was 88% when prediction file (sample_submisson.csv) is uploaded to their portal. The link for corpus/dataset download is given in blog-post.
It uses machine learning models to do sentiment polarity analysis on movie reviews. In other words, to classify opinions expressed in a text review (document) in order to determine whether the reviewer’s sentiment towards the movie is positive or negative.
It demonstrates the example of text classification and text clustering using K-NN and K-Means models based on tf-idf features.
The project is based on statistical analysis with R, which provides the survival prediction based on age,sex ratio,tickets,male,female,children etc.
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An Open Source Machine Learning Framework for Everyone
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Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
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We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.