Constructed supervised and unsupervised learning models using Python libraries like Scikit-Learn, TensorFlow, and Keras.
Developed machine learning models and algorithms for predictive analytics.
Utilized a variety of techniques including linear regression, logistic regression, decision tree, random forest, gradient boosting, and neural networks.
Optimized existing codebase for better scalability and efficiency.
Maintained version control repositories such as Git or SVN.
Deployed trained models into production systems with tools like Docker containers or Kubernetes clusters.
Developed software for embedded systems, coding solutions for both new installations and in-situ hardware.