Buddhikailabs's Stars
Asabeneh/30-Days-Of-Python
30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
kk7nc/Text_Classification
Text Classification Algorithms: A Survey
niconielsen32/ComputerVision
codebasics/deep-learning-keras-tf-tutorial
Learn deep learning with tensorflow2.0, keras and python through this comprehensive deep learning tutorial series. Learn deep learning from scratch. Deep learning series for beginners. Tensorflow tutorials, tensorflow 2.0 tutorial. deep learning tutorial python.
tunguz/ML_Resources
GitHub Repo with various ML/AI/DS resources that I find useful
adsieg/text_similarity
Text Similarity
ongteckwu/Resume-Rater
Rates the quality of a candidate based on his/her resume using unsupervised approaches
aniass/Product-Categorization-NLP
Multi-Class Text Classification for products based on their description with Machine Learning algorithms and Neural Networks (MLP, CNN, Distilbert).
massanishi/document_similarity_algorithms_experiments
Document similarity algorithms experiment - Jaccard, TF-IDF, Doc2vec, USE, and BERT.
ViAsmit/YOLOv5-Flask
miladsoltany/Face-Detection
Face detection using yolov5
manishshettym/ResumeRise
resumerise: classify and summarizes resumes
jonathanoheix/Sentiment-analysis-with-hotel-reviews
Sentiment analysis and text classification with Python
eitrheim/Resume-Screening-and-Selection
An Intelligent System to Automate Candidate Selection for Interview
mick-zhang/Amazon-Reviews-using-Sentiment-Analysis
iAmEthanMai/mask-detection-dataset
YOLOv5 image dataset 🔢
dblilienthal/Multiclass-Text-Classification-with-DistilBERT-on-COVID-19-Tweets
I implement a deep learning network to classify COVID-19 Tweets into 5 categories and 3 categories using DistilBERT (a lighter version of BERT) as an embedding layer along with an LSTM and Dense Layer. I Achieve 65% accuracy with 5 categories and 80% accuracy on 3 categories.
rsreetech/TextClassificationLSTMTensorFlow
SadmanSakib93/ANN-Stratified-K-Fold-Cross-Validation-Keras-Tensorflow
This repo contains examples of binary classification with ANN and hyper-parameter tuning with grid search.
easonlai/pneumonia_classification_challenge_databricks
This is sample repos for how to use Keras Tuner to perform hyper-parameter tuning in Databricks.
SinghHarshita/recruitment_system
A web-based recruitment system, chief highlight being system generated applicant ranking based on their CV.
TurboJapuraEfac/IBM-AI-ENGINEER-Machine-Learning-with-Python
Course files and exercises of IBM AI ENGINEER professional certification on coursera. Here you can find all the python code files and basic theory of major machine learing algorithms regression, classification, clustering and recommendation such as Agglomerative clustering Collaborative Filtering on Movies ,Content-based Recommendation Systems, DBSCAN Clustering ,Decision trees ,KNN ,Kmeans ,Linear Regression ,Logistic Regression , Multiple linear regression ,Non Linear Regression and SVM
AdirthaBorgohain/LSTM-Classifier-with-Hyperparameter-Tuning
A simple bidirectional LSTM Classifier to classify sentiments on a text. Hyperparameter tuning is done using Randomized CV Search to find best parameters for the deep learning model.
shivanishimpi/blogs
👩🏻💻 This repository is an archive for the code used in my blogs or YouTube (live) video sessions.
bassel-94/BERT
The application of CamemBERT for multiclass multilabel classification for Groupama
TurboJapuraEfac/NLP-Project
aimanh22/BERT_Multiclass_Emotion
anshulj97/Keras-Tuner--Hyperparameter-Tuning
Tuned the various hyper parameters in a Fashion MNIST classification model using Keras Tuner to get the best model with optimum parameter values to achieve higher validation accuracy.
Janani-Sankarasubramanian/Yelp-Case-Study
Our project is mainly focused on restaurants in Toronto. We analyze the factors that go into running a successful restaurant business. This study is aimed at helping low-rated restaurants turn their businesses into successful ones. We do so, by analyzing large datasets of popular restaurants based on high-quality reviews and try and identify some of the common underlying factors that go into making them successful. We then try and suggest these factors to poorly performing restaurants to help them improve their business. While we avoid some major pointers such as more investment or better quality of food (which is primarily one of the reasons), we evaluate other attributes of a successful restaurant business such as the amenities offered, food delivery services, variety of cuisines etc.
Lokesh2703/hospital
Hospital Management System