About Ajit Jaokar

#Datascience, #IoT, #MachineLearning, #BigData, Mobile,#Smartcities, #edtech (@feynlabs + @countdowncode) Teaching (@forumoxford + @citysciences). Head of AI for Smart cities lab in Madrid.

Ajit Jaokar Posts (21)

  • 3 Main Approaches to Machine Learning Models - 11 Jun 2019
    Machine learning encompasses a vast set of conceptual approaches. We classify the three main algorithmic methods based on mathematical foundations to guide your exploration for developing models.
  • Gold BlogLearning mathematics of Machine Learning: bridging the gap - 28 Sep 2018
    We outline the four key areas of Maths in Machine Learning and begin to answer the question: how can we start with high school maths and use that knowledge to bridge the gap with maths for AI and Machine Learning?
  • Gold BlogPlatinum Blog7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning - 17 Apr 2018
    It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help.
  • Is an AI /machine-driven world better than a human driven world? - 07 Mar 2018
    On the positive side of AI we have a prospect of self-driving cars, and other benefits, and thru education humans can evolve and improve. The risks include loss of jobs, growing inequality, dealing with superintelligence.
  • Data Science –The need for a Systems Engineering approach - 05 Oct 2017
    We need a greater emphasis on the Systems Engineering aspects of Data Science. I am exploring these ideas as part of my course "Data Science for Internet of Things" at the University of Oxford.
  • AI for fintech course – Early discounts and limited places - 20 Jun 2017
    This new course with limited places will focus on AI design (product, development and Data) for the fintech industry and will be taught online by Ajit Jaokar and Jakob Aungiers.
  • The dynamics between AI and IoT - 18 Apr 2017
    We see the need for a new type of Engineer who will combine knowledge from Electronics & IoT with Machine learning, AI, Robotics, Cloud and Data management (devops).
  • Silver BlogContinuous improvement for IoT through AI / Continuous learning - 25 Nov 2016
    In reality, especially for IoT, it is not like once an analytics model is built, it will give the results with same accuracy till the end of time. Data pattern changes over the time which makes it absolutely important to learn from new data and improve/recalibrate the models to get correct result. Below article explain this phenomenon of continuous improvement in analytics for IoT.
  • For AI Engineers/Data Scientists: Implementing Enterprise AI course - 07 Nov 2016
    This unique course that is focussed on AI Engineering / AI for the Enterprise. Created in partnership with H2O.ai , the course uses Open Source technology to work with AI use cases. It is offered online and also in London and Berlin, starting January 2017.
  • Gold BlogData Science for Internet of Things (IoT): Ten Differences From Traditional Data Science - 26 Sep 2016
    The connected devices (The Internet of Things) generate more than 2.5 quintillion bytes of data daily. All this data will significantly impact business processes and the Data Science for IoT will take increasingly central role. Here we outline 10 main differences between Data Science for IoT and traditional Data Science.

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