Data Science & Machine Learning
When I first self-taught myself ‘data science,’ there wasn’t a lot on the Internet to help me. I spent years cobbling information together reading what I could find about it. Now, there’s a plethora of Data Science and Machine Learning education available. There’s forums, open source libraries and much much more. Most of it is free and damn good. There’s no better time for a non data scientist or machine learning wannabe to learn about it, if you want to put in the time in.
I just stumbled across Jason Maye’s presentation on Machine Learning 101. Jason is from Google and he does a bang up job of explaining what features (attributes) are, the basics of machine learning, what is AI vs Machine Learning vs Deep Learning, and much more.
AI vs Machine Learning vs Deep Learning
He touches on many commonly used algorithms like multilayer preceptrons, k-nn, decisions, trees reinforcement learning, and even good old linear regression. He even embeds some great videos on how all this works and recommends that you set aside 2 hours in a quiet room to listen/read/watch his presentation.
How Google Uses Machine Learning
Bonus: The presentation is a goldmine and I recommend you view it in it’s entirety. There’s quite a bit of discussion on TensorFlow too. For any newcomers here, TensorFlow is a powerful deep learning package created by Google. It’s used in a lot of text analytics and vision related use cases. It’s written in C++ and is meant to use GPU’s and TPU’s for faster processing. As of 2020, it’s now integrated tightly into the Keras package, so with a few lines of Python code you can use deep learning simply!