Charu Chhimpa
Software Engineer - E6 at Whatfix
Bengaluru, Karnataka, India
Summary
👤
Senior🎓
Top SchoolCharu Chhimpa is a seasoned full-stack Software Engineer with 10 years’ experience building web platforms and machine learning solutions, currently at Whatfix in Bengaluru. Trained at IIIT Vadodara and through Udacity nanodegrees, she blends strong Python and ML skills with practical web development, shipping features across frontend and backend stacks. Her open-source contributions to the well-known aima-python repository extend core AI teaching material with Random Forests, ensemble and linear learners plus additional search algorithms, highlighting both applied ML expertise and an interest in foundational AI. She has hands-on experience applying computer vision and ML to product problems, and a background in teaching and mentoring that complements her technical delivery.
9 years of coding experience
8 years of employment as a software developer
Machine Learning Engineer Nanodegree, Computer Science, Machine Learning Engineer Nanodegree, Computer Science at UdacityBachelor of Technology (BTech), computer science, 9, Bachelor of Technology (BTech), computer science, 9 at Indian Institute of Information Technology VadodaraIntermediate, Intermediate at Bihani Children's Academy
English, Hindi, Sanskrit
Stackoverflow
1reputation
330reached
0answers
1question
Github Skills (14)
jupyter-notebook10
machine-learning10
ensembles10
python10
ai10
scikit-learn9
scikit9
linear-models9
algorithm9
algorithms9
data-structures8
data-structure8
iframe6
electron6
Programming languages (2)
Github contributions (5)
aimacode/aima-python
Mar 2018 - Mar 2018
Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
Role in this project:
Data Scientist
Data ScientistContributions:5 commits, 9 PRs, 24 comments in 7 days
Contributions summary:Charu significantly contributed to the implementation of machine learning algorithms within the repository. They added learners such as Random Forest, Ensemble Learner, and Linear Learner to the learning module, focusing on expanding the available AI techniques. Their work involved modifications to the existing `learning.ipynb` notebook, incorporating new functionalities and code examples, and thereby extending the project's ability to demonstrate and apply AI concepts. Moreover, the user added algorithms related to search such as Depth Limited Search and Iterative Deepening Search within the search notebook.
pythonpython-implementationapproachartificialmachine-learning
CharuChhimpa/WebApp_landing-page
Jun 2016 - Dec 2020
Contributions:5 pushes, 1 branch in 4 years 6 months
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