This page provides a list of certifications and courses I completed. Click here to download my CV and check out my portfolio to see my work.

This page provides a list of certifications and courses I completed. Click here to download my CV and check out my portfolio to see my work.

This page overviews my certifications and courses:

Cloud services

  • AWS Certified Machine Learning Specialty
    Amazon Web Services
  • Summary: Demonstrated in-depth understanding of AWS Machine Learning services. Learned to build, train, tune, and deploy ML models using the AWS Cloud. Showed the ability to derive insight from AWS ML services using either pretrained models or custom models built from open-source frameworks.

  • AWS Certified Developer - Associate
    Amazon Web Services
  • Summary: Showed a comprehensive understanding of application life-cycle management. Demonstrated proficiency in deploying with a CI/CD pipeline and using containers. Showed ability to develop, deploy and debug cloud-based applications that follow AWS best practices.

  • AWS Certified Solutions Architect - Associate
    Amazon Web Services
  • Summary: Gained a comprehensive understanding of AWS services and technologies. Demonstrated the ability to design well-architected cloud solutions that are scalable, secure, resilient, efficient and fault-tolerant.

  • AWS Certified Cloud Practitioner
    Amazon Web Services
  • Summary: Gained a fundamental understanding of IT services and their uses in the AWS Cloud. Demonstrated cloud fluency and foundational AWS knowledge. Learned to identify essential AWS services necessary to set up AWS-focused projects.

Machine Learning

  • Machine Learning Engineer Nanodegree
  • Summary: Learned software engineering and object-oriented programming practices and developed an open-source Python package for data processing. Deployed Machine Learning and Deep Learning models using Amazon SageMaker. Used API Gateway and Lambda to integrate deployed models into interactive web apps.

  • Deep Learning Nanodegree
  • Summary: Learned theoretical foundations of Deep Learning. Implemented different neural network architectures from scratch. Developed PyTorch modeling pipelines using CNNs, RNNs and LSTMs for a variety of prediction tasks. Deployed the trained models on Amazon SageMaker.


  • Algorithmic Toolbox
    University of California, San Diego
  • Summary: Learned key algorithmic techniques and concepts arising frequently in practical applications, including sorting and searching, divide and conquer, greedy algorithms, dynamic programming and recursion. Implemented algorithms to solve a variety of computational problems in Python and analyzed their running and memory complexity.

  • Data Structures
    University of California, San Diego
  • Summary: Learned common data structures used in various computational problems, including arrays, linked lists, stacks, queues, hash tables and trees. Analyzed typical use cases for these data structures and complexity of common operations. Practiced implementing data structures in Python programming assignments.

  • Data Structures and Algorithms with Python
  • Summary: Reviewed basic data structures and algorithms and extensively practiced their implementation in Python. Practiced analyzing running and memory complexity of different data structures and algorithms.


  • SQL for Data Science
    University of California, Davis
  • Summary: Learned fundamentals of SQL for Data Science purposes, including extracting, manipulating and combining data. Covered filtering, sorting and aggregating functionality. Practiced subqueries and table joins. Solved a variety of SQL programming tasks.