The Internet of Things (IoT) Show with Bruce Sinclair

See the complete show analysis notes at: http://www.iot-inc.com/deep-dive-internet-of-things-predictive-analytics-podcast

OK, get ready for it, we’re going to get down and dirty with predictive analytics and when I say dirty, I mean the mathematics of the different forms of predictive models dirty. Geek fest? Yes, but close your eyes and extrapolate how predictive analytics can be applied to your situation. By understanding how it works you will also understand the limits of what it can and cannot do. In this episode of the IoT Business Show I deep dive with Anil Gandhi and emerge with a better understanding of predictive analytics and how it really relates to real-time and descriptive analytics.

 

Read the rest of the show analysis notes at: http://www.iot-inc.com/deep-dive-internet-of-things-predictive-analytics-podcast

 

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If you have been enjoying this podcast for a while, please give it a review.  Click here to open iTunes where you can leave a one-click 5-star review or add your thoughts if you have more to say. If you use Stitcher Radio, you can do the same, here.  Thanks, iTunes reviews really help podcasts get noticed.

 

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Does anything produce more value in IoT than data science?

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Direct download: Predictive_Analytics_Deep_Dive__The_Shape_of_Things_to_Come.mp3
Category:general -- posted at: 12:07am PDT

The IoT Business Show is brought to you by: http://www.iot-inc.com


AI today is getting better at learning. In fact, learning is what differentiates what we call AI from advanced analytics. Machine learning algorithms minimize an error function by autonomously and iteratively adjusting their model variables. But what’s next for AI and machine learning? In this episode of the IoT business show I speak with James Canton, who in a meta way makes predictions about this prediction technology.


Read the rest of the show analysis notes including the transcripts at: http://bit.ly/IoTPodcast110notes


Consider taking 60 seconds to leave an honest review and rating for the podcast on iTunes. They are extremely helpful when it comes to ranking the show and you can be sure I read every one.


And lastly, don't forget to subscribe to the podcast to get automatic updates every time a new episode goes live.

 

Direct download: Making_Predictions_About_the_Predictive_Tech_of_ML_Part_2.mp3
Category:general -- posted at: 6:55am PDT

The IoT Business Show is brought to you by: http://www.iot-inc.com


AI today is getting better at learning. In fact, learning is what differentiates what we call AI from advanced analytics. Machine learning algorithms minimize an error function by autonomously and iteratively adjusting their model variables. But what’s next for AI and machine learning? In this episode of the IoT business show I speak with James Canton, who in a meta way makes predictions about this prediction technology.


Read the rest of the show analysis notes including the transcripts at: http://bit.ly/IoTPodcast110notes


Consider taking 60 seconds to leave an honest review and rating for the podcast on iTunes. They are extremely helpful when it comes to ranking the show and you can be sure I read every one.


And lastly, don't forget to subscribe to the podcast to get automatic updates every time a new episode goes live.

 

Direct download: Making_Predictions_About_the_Predictive_Tech_of_ML_Part_1.mp3
Category:general -- posted at: 5:05am PDT

See the complete show analysis notes at: http://www.iot-inc.com/three-main-classes-internet-of-things-data-analytics-podcast

In the Internet of Things there are generally three classes of analytics performed: real-time analytics done of the fly alerting you to anomalies; predictive analytics performed as a post process yielding a prediction and confidence level and descriptive analytics that reports on past, present or future data with visualizations that often result in the biggest insights. In this episode of the IoT Business Show I discuss the differences in these types of analyses with Shepherd Shi, as well as the steps that are taken before and after. 

 

Read the rest of the show analysis notes at: http://www.iot-inc.com/three-main-classes-internet-of-things-data-analytics-podcast

 

Help Spread the Word

If you have been enjoying this podcast for a while, please give it a review.  Click here to open iTunes where you can leave a one-click 5-star review or add your thoughts if you have more to say. If you use Stitcher Radio, you can do the same, here.  Thanks, iTunes reviews really help podcasts get noticed.

 

Ways to Subscribe to the IoT Business Show

Like what you hear?  Subscribe to get each episode delivered to your device via iTunes, Google PlayStitcher Radio or RSS (non-iTunes feed).

 

Of value? Help by sharing on:

 

Have an opinion? Join the discussion in our LinkedIn group

Did we miss anything important when describing the 3 main classes of IoT analytics?

Click here if you have an opinion on this podcast or want to see the opinion of others

 

Direct download: Makin_Bacon_and_the_Three_Main_Classes_of_IoT_Analytics.mp3
Category:general -- posted at: 9:39pm PDT

The IoT Business Show is brought to you by: http://www.iot-inc.com


Convoluted Neural Networks or CNNs are a type of AI typically used in computer vision to process images, but they are also applicable to process the time series data we typically get from sensors in IoT. In this episode of the IoT show I speak with Simon Crosby about how these CNNs can be used to make predictions about the future and reduce the massive amounts of data we collect to just the important stuff.


Read the rest of the show analysis notes including the transcripts at: http://bit.ly/IoTPodcast109notes


Consider taking 60 seconds to leave an honest review and rating for the podcast on iTunes. They are extremely helpful when it comes to ranking the show and you can be sure I read every one.


And lastly, don't forget to subscribe to the podcast to get automatic updates every time a new episode goes live.

 

Direct download: internet_of_things_business_podcast_109.mp3
Category:general -- posted at: 9:25pm PDT

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