Feature engineering, cleaning, and transformation datasets with Pandas, NumPy, Matplotlib.

Traditional algorithms (RF, KNN, SVM, XT) and dimensionality reduction (PCA, t-SNE). Sklearn models (XGB, LGBM, Cat) and competition techniques like blending, stacking.

PyTorch, common models (CNN, RNN, ResNet, LSTM), graph neural network (GCN, GAT, building graph with PyG), and including pre-training, transfer learning.

Hands-on experience with LLMs (Llama3, BERT) Text Classification and Generation .

Knowledge of PEFT and SFT, utilize quantization and fine-tune (QLoRA) LLMs for acceleration.

NLP (NLTK, HMM, Word2Vec), image processing (detection, segmentation).

ARIMA/DARIMA, differencing, ACF, and PACF analysis for Time Series forecasting.

Familiar with Linux systems, Git, MySQL.

Built online services with Django, HTML, CSS, and JavaScript.