Certifications

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 including:



Machine Learning

  • 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.

  • Machine Learning Engineer Nanodegree
    Udacity
  • 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
    Udacity
  • 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.



Coding

  • 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
    Codecademy
  • 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.



Databases

  • 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.