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Intro to Machine Learning and AI based on high school knowledge
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.
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3 Main Approaches to Machine Learning Models
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.
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 7 Books to Grasp Mathematical Foundations of Data Science 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.
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Data Science –The need for a Systems Engineering approach
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.
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The dynamics between AI and IoT
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).
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 Continuous improvement for IoT through AI / Continuous learning
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.
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 Data Science for Internet of Things (IoT): Ten Differences From Traditional Data Science
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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The Evolution of IoT Edge Analytics: Strategies of Leading Players
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.
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How to Become a (Type A) Data Scientist
This post outlines the difference between a Type A and Type B data scientist, and prescribes a learning path on becoming a Type A.
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Data Science of IoT: Sensor fusion and Kalman filters, Part 2
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.
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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