#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.
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.
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.
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.
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.
This article explores the significance and evolution of IoT edge analytics. Since the author believes that hardware capabilities will converge for large vendors, IoT analytics will be the key differentiator.
The second part of this tutorial examines use of Kalman filters to determine context for IoT systems, which helps to combine uncertain measurements in a multi-sensor system to accurately and dynamically understand the physical world.
7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning
Continuous improvement for IoT through AI / Continuous learning
Data Science for Internet of Things (IoT): Ten Differences From Traditional Data Science
How to Become a (Type A) Data Scientist