Machine Learning Recipes with Josh Gordon
最近开始研究Tensorflow,谷歌开发者 Josh Gordon自己做了一套视频教程，每月一更新，感觉讲解的非常好，所以就follow了这个教程，下面是对应的网站和代码笔记。
注意：因为是youtube上的资源，所以需要翻墙，可以借助shadowsock翻墙软件
这里是shadowsock for windows
Follows the Machine Learning Recipes with Josh Gordon series by Google Developers.
Part 1 - Hello World
Six lines of Python is all it takes to write your first machine learning program! In this episode, we’ll briefly introduce what machine learning is and why it’s important. Then, we’ll follow a recipe for supervised learning (a technique to create a classifier from examples) and code it up.
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Part 2 - Visualizing a Decision Tree
Last episode, we treated our Decision Tree as a blackbox. In this episode, we’ll build one on a real dataset, add code to visualize it, and practice reading it - so you can see how it works under the hood.
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Part 3 - What Makes a Good Feature?
Good features are informative, independent, and simple. In this episode, we’ll introduce these concepts by using a histogram to visualize a feature from a toy dataset.
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Part 4 - Let’s Write a Pipeline
In this episode, we’ll write a basic pipeline for supervised learning with just 12 lines of code. Along the way, we’ll talk about training and testing data. Then, we’ll work on our intuition for what it means to “learn” from data.
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Part 5 - Writing Our First Classifier
Welcome back! It’s time to write our first classifier. This is a milestone if you’re new to machine learning. We’ll start with our code from episode #4 and comment out the classifier we imported. Then, we’ll code up a simple replacement - using a scrappy version of k-Nearest Neighbors