I’ve always loved numbers and data. As a kid, I had my own stat tracking book for my sports nintendo games because I was obsessed with analyzing the results. I literally tracked hundreds of games worth of data for fun. I think that’s why I’ve recently become extremely interested in data analytics.
This book was suggested for people new to predictive analytics (PA) and machine learning so a decent place to start. Although it’s quite deep and used for university classes, it didn’t have a text book type vibe. One of the best parts was the massive amount of examples of how PA is used. From Facebook using PA to predict which posts you’ll like or IBM predicting whether sales events will meet targets. The examples are fantastic but Siegel also does a great job breaking down how it actually works.
You’ll learn about data, machine learning, models and how they all interconnect. “Machine learning processes data to produce a predictive model” is one of the simple explanations he uses. You learn about when to know a model is working and how much data is too much or too little. Learning about the nuances that went into teaching “Watson”, the IBM super computer was mind blowing. The speed at which it analyzes data is truly revolutionary.
I learned a lot from this book and highly recommend it for anyone curious about predictive analytics and machine learning. I think it provides a great foundation of understanding and something to build off of. It also provides a lot of resources on how to continue learning more about PA.