The customized-dash from baonhi3008

Hi there 👋

Hi, My name is Bao Nhi!

Hi there, My name is Bao Nhi, I’m a final year student in Information Technology at RMIT university Vietnam. I’m accountable, hard-working and aim to make something bigger than me. I am highly interested in AI application in education and biomedical engineering. Currently, I have been researching and learning many courses related to deep learning, applying machine learning algorithms in education and healthcare medical applications, which make me even more want to develop deeper understanding about these areas.

My interest areas:

Using deep learning for self-regulated learning
Using deep learning for image processing in healthcare application
Technology are AI applications and researches
Automation Software QA

More practical projects are available on my Github, please feel free to take a look.

Projects

Machine Learning and Big Data project

Machine Learning using Tensorflow

Detecting colorectal cancer using deep learning and supervised learning algorithms

In this work, a machine learning model must be implemented to aid in the detection of colorectal cancerous cells and the differentiation of cell types in colon cancer. There are two major tasks that need to be delivered:

Task 1: Cancerous Classification
Task 2: Cell Types Classification

I have reconstructed 5 CNN architectures to be customized to the model and perform 2 tasks with further detail analysis on each architecture to see if it is good to solve the problem

Supervised Machine Learning methods: XGBoost, LightGBM, CatsBoost
CNN architectures: AlexNet, VGG-16, ResNet-50, Xception, RCCNet
Language used:

Machine Learning approach to predict if patient develop sepssis during their stay at ICU

Predict sepssis during ICU stay using different ML Classification methods

In the work, I developed more than 6 models from basic to advanced with 3 main algorithms: Decision Tree, Random Forest, Logistic Regression. Each model was developed from basic to advanced with further techniques including SMOTE, SMOTE-ENN, Cross Validation with Grid Search. At the end of each model, I made an analysis and evaluation for the performance and explain why further technique needed to be applied for better performance of the model. Finally, I made a ultimate judgement and analysis to see the which one is the best model for given problem.

Machine Learning Methods and Preprocessing Technique
Decision Tree
Random Forest
Logistic Regression
SMOTE + Random Under Sampling
Language used:

Building big data pipeline with Docker, Kafka and Cassandra

In this work, I have deployed an end-to-end data pipeline on your local computer using Docker containerized Kafka (data streaming), Cassandra (NoSQL database) and Jupyter Lab (data analysis Visualization). This wrok is intended to give practical experience with a simple big data stack and data pipeline.

Language used:

Professional Technical Experience

Technical Support Specialist 4/2021 - 6/2022

In 2021 I started my position as Technical Service Support Specialist at Skedulo - company that provide Human Management applications and software.

My responsibilities

Supporting the technical customer support team to investigate the error and solve customer application's technical issue.
Conduct testing on current product including SalesForces, web application and mobile application to verify the error.
Finding solution to the issue from client. Working with the developer team to fix bug if any.
Communicate with client to make sure that the support team is fully understand the issue and deliver the expected outcome for the client.

Salesforces Manual Tester 11/2021 - 3/2022

In November 2021, I started working as IT consultant for Net company, my main work was Salesforces Manual Tester.

My responsibilities

Conducting unit test on ready-to-release function and feature.
Writing test case for features in Salesforce platform.
Evaluating test cases based on company customised framework.
Writing weekly report for all test cases executed in the week including pass, fail and must-be-review test cases.