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1.ML/NLP/Speech/CV with Python, 2.Python coding skills (leetcode, pandas, Airflow, PySpark, steamlit, and web-scraping, etc)
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Python 1.70%
python's Introduction
NLP & machine learning with Python
NLP basics: N-grams, POS tagging, lemmatizer, data cleaning with both regular expression and stopwords, TF-IDF vectors, and Sentiment analysis
Name entity recognition, clustering models (e.g., K-means, LDA, and BERT) and visualizations, keyword extraction, and sentence similarity search
SVM classification with scikit-learn
bi-LSTM classification with keras (my dissertation code)
Grammar correction in OpenNMT-py (submitted for NLPCC GEC shared task)
Text Summarization with Encoder-decoder Transformer in Pytorch
Text Summarization with T5 (Huggging Face)
Syntactic trees and lexical semantic features
Simple perceptron classification
Speech graph functions (following team product standard and unit testing procedure)
Speech recognition with tensorflow using CNN and bi-GRU
XGBoost classification and tuning
Some basic recommendation systems, including tfidf-embedding & MF/SVD (under construction)
Use a local Neo4j data for queries and exploratory graph analyses like centrality, community, and similarity
includes my coding and learning on how to use, fine-tune, and apply CV models such as image classification and facial recognition
My dev directory on text summarizers, including frequency-based extractive models, transformer with BERT embedding, and BART
Feature extraction of some common speech features like intensity, MFCC. center of gravity, skewness, HNR, jitter & shimmer
This folder contains airflow CLI notes, a demo case of a SQL-based ETL pipeline, and a demo of a customized pipeline directed acyclic graph (DAG) plugin
Practice on DSA coding challenges
My notebooks on pandas and PySpark
notes and miniproject on topics of OOPs (still updating): class, dunders, inheritance
my code on using playright and asyncio to scrap job posts at a large scale
put simple NLP code onto streamlit UI
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