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$20 USD / saat
 SINGAPORE bayrağı
singapore, singapore
$20 USD / saat
Şu anda burada saat 8:13 ÖS
Şubat 16, 2021 tarihinde katıldı
2 Öneriler

Nicole T.

@tansimin2591

 4,8 (2 değerlendirme)
1,4
1,4
100%
100%
$20 USD / saat
 SINGAPORE bayrağı
singapore, singapore
$20 USD / saat
%100
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%100
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Tekrar İşe Alınma Oranı

Research officer, Bsc.biomedical engineering

I am a data scientist working as a research officer for many years. I have been working since after graduation on MATLAB-related projects. Mainly dealing with biomedical signals, I have also dealt with prediction, anomaly detection and text analysis project using python. I specialize in machine learning algorithm and doing prediction analysis. I have been doing data visualization and analysis for 5 years. With a diverse background in machine learning, I bring a multi-disciplinary subject together and make use of creativity, technical expertise, and a results-driven mindset to every project I have done. My specialization is in medical data and analysis and I enjoy being thrived on setting out challenging tasks. My 5 years of experience have equipped me with the ability to create unusual approaches to machine learning and predictive analysis models. Whether it's structured data or unstructured data , I've consistently delivered exceptional results by using my unique machine-learning approaches. I ensure that there is no data leakage and that desirable results are obtained for all my projects My skillset include: - Beautiful Soup - genism - SHAP - LIME - LDA - transformers -tkinter - Keras - PyTorch - torchvision - Bertopic - NLTK - gradio - JQuery - HTTP Server - CSS - HTML • Flask • Rasa NLU • Rasa core • RasaX • Natural Language Processing • sklearn.preprocessing • sk Learn.model_selection • sk learn. metrics • SK learn • Seaborne • Os • Scipy • pygame • Tkinter • Keras • Scipy • Matplotlib • Pandas • Numpy • SckitLearn • Skimage • Pywavelet • OpenCV • MatPlotlib • Tensorflow • Wavelet Scattering Tool • Fuzzy Logic controller • Deep Learning • Global Optimization Tool • QRS peak detection Analysis Tool • Heart Rate Variability Analysis Tool • Recurrent Quantification Analysis Tool • Point Care Plot • Wavelet Transform The language i had mastered are: • MATLAB • Jupyter • Anaconda Environment • Minitab 20 • R programming • Octave - Python
Freelancer Python Developers Singapore
Python Developers freelancerlarından Singapore içinden olanlarının tümünü görün.

Nicole T. ile işiniz hakkında iletişime geçin

Detayları sohbet üzerinden tartışmak için giriş yapın.

Portföy

11599041
- Load the datatset - Clean and preprocess text - Concatenate train and test set - Tokenization - Label encode categorical data - Split ratio 8: 2 - Build DNN model - Model Evaluation - Build fuzzy logic controller. - Analyze sentiment polarity. - Design app application
Sentimental Analysis using Deep neural network and fuzzy
11598184
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
11596645
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - Combine sentence embedding with word embedding model - BERT encoding - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
Sentiments and Text Analysis
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - Combine sentence embedding with word embedding model - BERT encoding - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
Sentiments and Text Analysis
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - Combine sentence embedding with word embedding model - BERT encoding - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
Sentiments and Text Analysis
11595865
- Cleaning messy and noisy data in a cancer dataset with missing value and redundant data - Data Visualization using normal distribution - Categorize the number of patients with disease and non-disease cases in the dataset - Feature Extraction using pyRQA, recurrent quantification analysis - Classification of dataset using Supervised learning classification, KNN, SVM and DT
Breast Cancer Prediction using Machine Learning Technique
- Cleaning messy and noisy data in a cancer dataset with missing value and redundant data - Data Visualization using normal distribution - Categorize the number of patients with disease and non-disease cases in the dataset - Feature Extraction using pyRQA, recurrent quantification analysis - Classification of dataset using Supervised learning classification, KNN, SVM and DT
Breast Cancer Prediction using Machine Learning Technique
- Cleaning messy and noisy data in a cancer dataset with missing value and redundant data - Data Visualization using normal distribution - Categorize the number of patients with disease and non-disease cases in the dataset - Feature Extraction using pyRQA, recurrent quantification analysis - Classification of dataset using Supervised learning classification, KNN, SVM and DT
Breast Cancer Prediction using Machine Learning Technique
11595859
- Building of CNN-naïve baye classifier using a 2D image from feature extraction - Wavelet scattering algorithm for feature extraction and mapping to 2D images. - Extraction of CNN layer as a feature in another model classification - Denoising ECG signal - 1 min ECG, 18 NSR vs 18 sleep apnea ECG
A Deep Learning Framework for Automatic Sleep Apnea
11593505
- Allow other users to test my RASA bot and gain feedback from using RASA X - Create forms in RASA chatbot e.g., Client Particular Form, Appointment Form - integrating different field of specialization into one chatbot database, (Healthcare, work-stress, basic consultation, mental health survey, tracking your health condition and feeling) - integrate voice assistant in AI chatbot - create webpage with local host and deploy speech recognition AI chatbot - create html webpage for chatbot and deployment using jQuery. - Deploy Rasa chatbot with flask
Deployment of chatbot
11599041
- Load the datatset - Clean and preprocess text - Concatenate train and test set - Tokenization - Label encode categorical data - Split ratio 8: 2 - Build DNN model - Model Evaluation - Build fuzzy logic controller. - Analyze sentiment polarity. - Design app application
Sentimental Analysis using Deep neural network and fuzzy
11598184
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
11596645
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - Combine sentence embedding with word embedding model - BERT encoding - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
Sentiments and Text Analysis
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - Combine sentence embedding with word embedding model - BERT encoding - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
Sentiments and Text Analysis
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - Combine sentence embedding with word embedding model - BERT encoding - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
Sentiments and Text Analysis
11595865
- Cleaning messy and noisy data in a cancer dataset with missing value and redundant data - Data Visualization using normal distribution - Categorize the number of patients with disease and non-disease cases in the dataset - Feature Extraction using pyRQA, recurrent quantification analysis - Classification of dataset using Supervised learning classification, KNN, SVM and DT
Breast Cancer Prediction using Machine Learning Technique
- Cleaning messy and noisy data in a cancer dataset with missing value and redundant data - Data Visualization using normal distribution - Categorize the number of patients with disease and non-disease cases in the dataset - Feature Extraction using pyRQA, recurrent quantification analysis - Classification of dataset using Supervised learning classification, KNN, SVM and DT
Breast Cancer Prediction using Machine Learning Technique
- Cleaning messy and noisy data in a cancer dataset with missing value and redundant data - Data Visualization using normal distribution - Categorize the number of patients with disease and non-disease cases in the dataset - Feature Extraction using pyRQA, recurrent quantification analysis - Classification of dataset using Supervised learning classification, KNN, SVM and DT
Breast Cancer Prediction using Machine Learning Technique
11595859
- Building of CNN-naïve baye classifier using a 2D image from feature extraction - Wavelet scattering algorithm for feature extraction and mapping to 2D images. - Extraction of CNN layer as a feature in another model classification - Denoising ECG signal - 1 min ECG, 18 NSR vs 18 sleep apnea ECG
A Deep Learning Framework for Automatic Sleep Apnea
11593505
- Allow other users to test my RASA bot and gain feedback from using RASA X - Create forms in RASA chatbot e.g., Client Particular Form, Appointment Form - integrating different field of specialization into one chatbot database, (Healthcare, work-stress, basic consultation, mental health survey, tracking your health condition and feeling) - integrate voice assistant in AI chatbot - create webpage with local host and deploy speech recognition AI chatbot - create html webpage for chatbot and deployment using jQuery. - Deploy Rasa chatbot with flask
Deployment of chatbot

Değerlendirmeler

Değişiklikler kaydedildi
2 değerlendirme içinden 1 - 2 arasındakiler gösteriliyor
 Değerlendirmeleri filtreleme kriteri:
4,6
$25,00 USD
Project 37671480 has been deleted
She was very supportive and worked well with EEG datasets.
Kullanıcı Avatarı
bayrağı Abdur R.
@rasol1
•
16 gün önce
5,0
₹2.000,00 INR
Brain Tumour Detection via Machine Learning
I am very satisfied with the work. My work has been completed within the given time as per my requirement .
Python
Matlab and Mathematica
Algorithm
Machine Learning (ML)
Data Mining
R
bayrağı Rohan S.
@rohan072
•
4 ay önce

Deneyim

Research officer

National Neuroscience Institute
Haz 2023 - Şu anda
Research on AI

Laboratory Technician

Nanyang Technological University
Ara 2013 - Şu anda
I do laboratory operation and management on daily job basis. In addition, I have been working with my professor in school for 4 year with project related to MATLAB. I graduated with a biomedical engineering in 2020

Eğitim

Biomedical engineering ( Part-time)

Singapore Institute of Management, Singapore 2017 - 2022
(5 yıl)

Nicole T. ile işiniz hakkında iletişime geçin

Detayları sohbet üzerinden tartışmak için giriş yapın.

Doğrulamalar

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Önde Gelen Beceriler

Python 1
Matlab and Mathematica 1
Machine Learning (ML) 1
Deep Neural Network 1
Neural Networks

Benzer Freelancerlara Göz Atın

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Python
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