"Revenues are up 5% due to your little geospatial search trick," the VP of Connected Services says, "You know your former boss's position is still open. Maybe we should fill it from the inside..."

Your pulse quickens, you were hoping he might come to this conclusion. You deserve a promotion after what your analytics has brought to the company. You now have one person working for you focusing on geospatial analysis. You can just imagine what you could do with a whole team.

"There is something that we have been toying around with though," he continues, "With all this data we are collecting, we should be able to tap into machine learning models to predict equipment failures. Some think we should be hiring an outside consulting company to handle all of it. Sounds expensive to me. I sure wish we could coordinate this ourselves, work with data scientists of our own choosing... know anyone that might be up to it?"

He winks and walks off, hands behind his back, whistling a Brahms tune.

In this chapter, we introduce data science techniques such as machine learning, deep learning, and forecasting using ARIMA. Special focus is given on how to use these methods with IoT data. The core concepts for each will be reviewed along with examples in R. Deep learning will be described along with where to go to create an Amazon EC2 instance with TensorFlow.

This chapter covers a lot of ground, so hold onto your hats.

This chapter covers the following topics:

• Machine learning (ML):
  • Core concepts
  • Feature engineering
  • Validation methods
  • The Bias-variance tradeoff
  • Comparing models to find the best fit
  • Random forests
  • Gradient boosting machines
  • Anomaly detection
• Forecasting using ARIMA
• Deep learning:
  • Use cases with IoT data
  • Setting up and running a simple deep learning model using TensorFlow