📊 Skills

• Proficient in mainstream time series forecasting algorithms, including Moving Average, Exponential Smoothing, XGBoost, ARIMA, Random Forest, VAR, GARCH, Prophet, and MTS-Mixers.
• Skilled in regression forecasting algorithms such as Ordinary Least Squares Linear Regression, Lasso Regression, Ridge Regression, SGD Regression, ElasticNet, LAR, OMP, Bayesian ARD Regression, Bayesian Ridge Regression, GLM, LightGBM, CatBoost, and DeepForest.
• Well-versed in machine learning algorithms including Logistic Regression, K-Nearest Neighbors (KNN), ensemble methods, Support Vector Machines (SVM), K-means clustering, Naive Bayes, Hidden Markov Models (HMM), and Conditional Random Fields (CRF); proficient in implementing these algorithms using the scikit-learn library.
• Proficient in deep learning frameworks and architectures such as RNN, LSTM, GRU, Seq2Seq, Attention, Transformer, and BERT.
• Familiar with classical CNN architectures including LeNet, LeNet-5, AlexNet, GoogLeNet, VGG-16, and ResNet.
• Experienced in Python development; proficient with scientific libraries such as NumPy, Pandas, and Matplotlib, as well as deep learning frameworks including PyTorch and TensorFlow.
• Knowledgeable in feature engineering, including data cleaning, feature extraction, feature selection, dimensionality reduction, and model evaluation.
• Familiar with Linux command-line operations and experienced in deploying projects in a Linux environment.