AI for Wellness


Wellness code logo.png


The quest for making sense to the data ocean generated bythe human body and taking insightful action to take chargeof one’s wellness is gaining significance in the community.The play of amazing computing speed, cloud infrastructureand the smartest of medical devices throw up unprecedentedchallenges and opportunities. Whether to enhance yourmind-body wellness through durable AI (ArtificialIntelligence) solutions or simply deploying them to your dailywellness chores, the power of AI had never received morerecognition. On the other hand, the new age as well astraditional healthcare professionals (doctors, practitioners,service providers, etc.) are all looking to embrace AItechniques for superior solutions, enhance awareness anddevelop actionable insights.


AI innovations are now being applied across all areas ofmedicine, but AI knowledge is not easily available to most medical professionals and practitioners. The same is true forothers who are ready to embark on their Wellness journeywith preventive care, lifestyle and dietary practices to empower themselves. 

This well-structured course is aimed to bridge the wideningknowledge and basic application gap faced by the community. The basic offerings of the course should help toswiftly grasp the core AI concepts and how they are appliedin the medical field for professionals as well as the section ofthe community, who are ready to improve the quality of theirlives. 

In this starter course

  • What is Artificial Intelligence and how does it work?

  • Core concepts such as Learning and Prediction, Algorithms, Models, and Training

  • Different types of AI algorithms and what they can be used for

  • Common use cases of AI in Medicine and advanced/state of the art AI techniques being developed for medicine

  • How to empower Wellness Explorers, Starters and Champions to make sense of AI in their daily chores and goals

  • Address issues of AI like Bias, Trust, Ability to Explain and Privacy in a healthcare and wellness context

  • Building some basic AIs (no coding required!!) to help practice and apply the concepts and further the learning

Child at the Doctor

Course structure

  • The course is made up of 8 modules, each 1 hour.

  • The course is taught live online via zoom by expert instructors. Curriculum designed by PhDs in Computer Science and Artificial Intelligence with added background on AI application in medical contexts

  • Each hour is spent in concepts, discussion, and somehands-on exploratory exercises

Doctor and Patient

After completion, partcipants will be able to:

  • Understand what AI is and how it is being applied in theirfield or needs to improve wellness

  • Be able to ask the right questions about how AI is beingused in their domain

  • Make their own decisions about which AI techniques aresuitable for them and which are not

  • Follow on to deeper areas of AI as they find interest to enroll in a tailored course in phase 2. This tailored course is for doctors and healthcare professionals who completed the Starter Course.

Schedule and Price

Starts July 1, 2021

Every Tuesday 6-7PM PT

8 Sessions

Price Here

Instructors and Credentials


Dr Nisha Talagala

CEO and founder of AIClub and Pyxeda AI


Previously, Nisha co-founded ParallelM whichpioneered the MLOps practice of managing machinelearning in production, acquired by DataRobot. Nisha isa recognized leader in the operational machine learningspace and has more than 20 years of expertise insoftware development, distributed systems, technicalstrategy, and product leadership. Nisha earned herPh.D. at UC Berkeley where she did research on clustersand distributed systems. Nisha co-chairs the annualconference on production machine learning (OpML).Nisha holds 73 patents in distributed systems andsoftware, is a frequent speaker at industry andacademic events, and is a contributing writer to Forbesand other publications.


Dr Sindhu Ghanta

Head of Machine Learning, AIClub


Sindhu received the M.S. degree from Texas TechUniversity in 2010 and the Ph.D. degree in electrical andcomputer engineering from Northeastern University,Boston, USA, in 2014. She was a Post-Doctoral Fellowwith BIDMC and the Department of Pathology, HarvardMedical School, where she was involved in the detectionand classification of features from histopathological(breast cancer) images. She worked as a researchscientist with Parallel Machines on monitoring thehealth of machine learning algorithms in production andhas many publications on ML innovations. She currentlyworks as the Head of Machine Learning for AIClub.