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This is Part 2 of a four-part series that breaks up a talk that I gave at the Toronto AI Meetup. Part 1 was all about the foundational concepts of machine learning. In this part I get into more advanced machine learning concepts. These include:  

• [00:13] Optimization (I explain calculus!!!) 
• [04:40] Gradient descent 
• [06:26] Perceptron (or linear models – we learned what these are in part 1 but I expound a bit more) 
• [07:04] Neural Networks (as an extension to linear models)
• [09:28] Brief Review of TensorFlow

Hope you enjoy Part 2! As always feel free to send any feedback or add any comments below if you have any questions.

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