Programming Languages: Python (proficient)
Machine Learning Algorithms: Supervised learning (e.g., regression, decision trees), unsupervised learning (e.g., clustering, PCA), reinforcement learning, ensemble methods (e.g., Random Forest, XGBoost)
Deep Learning: Familiar with frameworks such as TensorFlow, Keras, or PyTorch
Data Processing: Expertise in data wrangling, preprocessing, and feature engineering using libraries like Pandas, NumPy
Model Evaluation and Optimization: Experience with metrics for evaluating models (e.g., precision, recall, F1-score), hyperparameter tuning (e.g., GridSearchCV, RandomSearch)
Deployment: Familiarity with deploying ML models
Version Control: Proficient with Git and collaborative tools
Data Visualization: Strong command of Matplotlib, Seaborn, Plotly for insights presentation