#Datascience, #IoT, #MachineLearning, #BigData, Mobile,#Smartcities, #edtech (@feynlabs + @countdowncode) Teaching (@forumoxford + @citysciences). Head of AI for Smart cities lab in Madrid.
Machine learning information is becoming pervasive in the media as well as a core skill in new, important job sectors. Getting started in the field can require learning complex concepts, and this article outlines an approach on how to begin learning about these exciting topics based on high school knowledge.
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.
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?
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.
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.
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.
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.
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).
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.
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.
#Datascience, #IoT, #MachineLearning, #BigData, Mobile,#Smartcities, #edtech (@feynlabs + @countdowncode) Teaching (@forumoxford + @citysciences). Head of AI for Smart cities lab in Madrid.
Learning mathematics of Machine Learning: bridging the gap
7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning
Continuous improvement for IoT through AI / Continuous learning
