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Name: Dheeraj Rathee

Type: User

Company: Provide Digtial

Bio: Chief Technology and Analytics Officer @ Provide Digital

Location: United Kingdom

Hi there 👋 - I'm Dr Dheeraj Rathee

A curious learner and a passionate implementer in the field of AI and Data Science. I keep looking for new possibilities to test the power of AI in different application areas within healthcare.

I am passionate about quality product development and implementing cutting-edge technologies from the fields of AI and Data Analytics to enhance product USP and value to customers. I got 8+ years of experience in Research and Development in various technology fields and am always up to learn, teach, inspire, and be inspired by talented teams and interesting businesses that foster innovation. I am the recipient of the Global Exceptional Talent endorsement for AI and Data Analytics from Tech Nation.

Here are some key highlights about my professional journey:

Proven leadership experience and successful history of delivery of scalable, AI-driven digital platforms for healthcare providers in the UK.

Experience in building cross-functional, lean, or agile technology teams using internal and external, on and offshore resources from 1-2 to 50 strong.

Deep understanding of data protection and privacy compliance requirements.

Effective management of technology budgets and time frames

Extensive knowledge of Software Design and Development, Solution Architecture, and Project Management methodologies coupled with organisation and planning skills to ensure on-time delivery, at reduced cost and high quality.

Experience in contributing to the funding rounds

Connecting and engaging with key tech stakeholders, tech partners, and communities

My favourite pastime: reading (online blogs, books), watching Netflix, and spending time with my two supergirls :).

Happy to connect over these platforms:

Dheeraj Rathee's Projects

awesome-django

The Best Django Resource, Awesome Django for mature packages.

awesome-nlp

:book: A curated list of resources dedicated to Natural Language Processing (NLP)

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

awesome-python-applications

💿 Free software that works great, and also happens to be open-source Python.

bciner_errpdetection

bike-share-data-udacity

Bike Share Data Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they can also return it to the same location if they'd like to just go for a ride. Regardless, each bike can serve several users per day. Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used. In this project, you will use data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. You will compare the system usage between three large cities: Chicago, New York City, and Washington, DC. The Datasets Randomly selected data for the first six months of 2017 are provided for all three cities. All three of the data files contain the same core six (6) columns: Start Time (e.g., 2017-01-01 00:07:57) End Time (e.g., 2017-01-01 00:20:53) Trip Duration (in seconds - e.g., 776) Start Station (e.g., Broadway & Barry Ave) End Station (e.g. Sedgwick St & North Ave) User Type (Subscriber or Customer) The Chicago and New York City files also have the following two columns: Gender Birth Year

cheetsheets-ml

Important Cheat Sheets for machine learning and deep learning researchers and data scientists (Python and R)

deep_transfer_learning

We are going to present Deep and Transfer Learning

dheerajrathee

dlcovid19

fieldtrip

The MATLAB toolbox for MEG, EEG and iEEG analysis

hello_django

hmm-mar

Toolbox for segmentation and characterisation of transient connectivity

iads-summer-school-2021

iads_summerschool_2019

Decision Trees, Random Forests, and Gradient Boosting

intro_2_colab_python

megbci2020

A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface

mne-python

MNE : Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python

moabb

Mother of All BCI Benchmarks

nature-scientific-data-2021

A magnetoencephalography dataset for motor and cognitive imagery based brain computer interface

nics-firearm-background-checks

Monthly data from the FBI's National Instant Criminal Background Check System, converted from PDF to CSV.

project-sorting

random-forest-matlab

A Random Forest implementation for MATLAB. Supports arbitrary weak learners that you can define.

robobrowser

speaker-recognition-using-ann

tensorflow-for-detection-credit-card-fraud

Application of Deep Neural Network for fraud detection in credit card transactions.

xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow