Machine Learning | Deep Learning | Natural Language Processing | Big Data | Python | C# | JS | Hadoop |
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Keyword-Extraction-using-PYtextRank
November 25, 2019 – November 25, 2019
This is implementation of keyword extraction using PYtextRank library. It allows you to extract keywords from text. These are meaningful keywords.
View ProjectN-Gram-Topic-modelling-using-LDA
October 4, 2019 – October 4, 2019
It is python implementation of n-grams topic modelling.
View ProjectUnigram-Topic-Modelling
October 4, 2019 – October 4, 2019
This is implementation of topic modelling where topics will be unigram. It is basic implemented in python for understanding the concept of topic Modelling.
View ProjectImplementation-of-Convolutional-Neural-Network-on-MNIST-dataset
September 26, 2019 – September 27, 2019
This is the implementation of convolutional Neural network which applies a filter bank to image to get the features for neuron with sigmoid and cross entropy.
View ProjectMulti-layer-Neural-Network-for-multi-class-classification-of-MNIST-dataset-using-Softmax
September 26, 2019 – September 26, 2019
It is implementation of multi-layer neural network for multi class classification of MNIST dataset using Python language. You can understand the working of softmax.
View Projectsingle-Layer-Neural-Network-Implementation-in-Python
September 25, 2019 – September 25, 2019
It is implementation of multi layer neural network. Each layer have same number of neurons. It is using Sample dataset. This implementation is not for the commercial purpose instead it is for understanding and learning purpose
View ProjectPOS-Tagging-Sentiment-Analysis
September 16, 2019 – September 16, 2019
Sample Code for POS Tagging of english words and Sentiment Analysis for sentence using Blob
View ProjectUrdu-Spell-Checker-using-Noisy-Channel-Model
April 8, 2019 – April 11, 2019
It is Python Implementation of Word Spell Checker. Currently it is trained on Urdu/Hindi Dataset but you may train it on any language just by changing the path of corpus file.
View ProjectPython-Implementations-of-techniques-to-find-Distance-or-Similarity-Between-String
April 5, 2019 – April 5, 2019
In this Implementation we have code many techniques to find the distance or similarity between strings. Techniques that we explore and Implemented in Python are (word-level & character-level) Minimum Edit Distance, Euclidean Distance, Manhattan Distance, Chebychev Distance, Cosine Similairty, bigram-level Jaccard Similarity Hamming Distance and Longest Common Subsequence Algorithms.
View ProjectCultural Fit Analysis
The candidate's projects are heavily focused on academic implementations and personal learning, which indicates a strong drive for self-improvement and foundational understanding in data science. However, the lack of team projects, professional experience, or diverse application areas limits the assessment of cultural fit for a senior role requiring collaboration and business impact. The projects are very specific to NLP and basic neural networks, which might indicate a narrow focus for a senior Data Scientist role.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's projects are primarily personal and academic, offering limited insight into collaborative work or real-world operational challenges.