AI & Machine Learning Intern TensorFlow, PyTorch, Scikit-learn

**/**/****

083*******

***************@*****.***

***/*, */* ******, Dien Hong

Ward

https://github.com/LoveCters

Education

Open University (OU)

Computer Science

**** - *****

Third-year student

Skills

Tools: Jupyter Notebook, Google

Colab, VS Code, Git, GitHub

Deep Learning: TensorFlow,

PyTorch, Keras

Machine Learning: Scikit-learn

Model Optimization &

Hyperparameter Tuning

Data Processing:NumPy, Pandas,

Data Cleaning, Feature Engineering

Visualization: Matplotlib, Seaborn

English Skill: Ielts 6.0

Bùi Phong Sơn

AI Engineering Intern

Objective

Motivated 3rd-year Computer Science student seeking a Deep Learning internship to apply theoretical knowledge in AI/ML, develop practical skills with frameworks like TensorFlow/PyTorch, and contribute to impactful projects.

PROJECT

Stock Market Prediction ( 01/03/2025 - Present )

Github: https://github.com/LoveCters/Stock-Market- Prediction/tree/main

• Role: Sole Developer (Personal Project)

• Technologies Used:

-Deep Learning: TensorFlow, Keras (LSTM, Bidirectional LSTM, Dropout, Dense, L2 Regularization)

-Model Optimization: EarlyStopping, ReduceLROnPlateau, Adam Optimizer

-Data Processing: Pandas, NumPy, RobustScaler, TA-Lib (Technical Indicators)

-Evaluation Metrics: Accuracy, Precision, Recall, F1-Score

• Main Features:

- Outputs binary predictions (Up/Down) for the next trading day based on the trained model

Air Quality Index (AQI) Prediction ( 05/05/2025 - Present ) Github: https://github.com/LoveCters/Air-Quality-Index--AQI-- Prediction

• Role: Sole Developer (Personal Project)

• Technologies Used:

-Machine Learning: Scikit-learn (Linear Regression, MinMaxScaler, evaluation metrics)

-Deep Learning: TensorFlow, Keras (LSTM, Bidirectional LSTM, Dense, InputLayer)

-Model Optimization: EarlyStopping, ReduceLROnPlateau, ModelCheckpoint, Adam Optimizer

-Evaluation Metrics: MAE, RMSE, R Score

• Main Features:

- Forecasts future PM2.5 air pollution levels based on user-specified time intervals using LSTM models.

Interests

Reading, Fitness, Gaming,

Communication & Socializing

Fraud Detection ( 10/06/2025 - Present )

Github: https://github.com/LoveCters/Fraud-Detection

• Role: Sole Developer (Personal Project)

• Technologies Used:

-Visualization: Matplotlib, Seaborn

-scikit-learn: ColumnTransformer, OneHotEncoder, train_test_split, LogisticRegression

-XGBoost: XGBClassifier (hist, scale_pos_weight, early stopping)

-Feature engineering: rule-based flags

-Metrics & tuning: PR-AUC, ROC-AUC, Precision/Recall/F1, Confusion Matrix, precision_recall_curve

-Persistence: joblib (lưu preprocessing + model + threshold)

• Main Features:

- Detects fraudulent transactions from financial datasets using advanced machine learning algorithms.

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