Learn Machine Learning Zero to Hero!

Intro to Machine Learning (ML Zero to Hero, part 1)

Intro to Machine Learning: Machine Learning represents a new paradigm in programming, where instead of programming explicit rules in a language such as Java or C++, you build a system which is trained on data to infer the rules itself. But what does **ML **actually look like? In part one of Learn Machine Learning Zero to Hero, ***AI ***Advocate Laurence Moroney (lmoroney@) walks through a basic Hello World example of ***building ***an ML model, introducing ideas which we’ll apply in later episodes to a more interesting problem: computer vision.

Try this code out for yourself in the Hello World of Machine Learning: https://goo.gle/2Zp2ZF3

Basic Computer Vision with ML (ML Zero to Hero, part 2)

Basic Computer Vision with ML: In part two of Learn Machine Learning Zero to Hero, AI Advocate Laurence Moroney (lmoroney@) walks through basic computer vision with Machine Learning by teaching a computer how to see and recognize different objects.

Beyond Hello World, a Computer Vision Example: https://goo.gle/34cHkDk

Introducing convolutional neural networks (ML Zero to Hero, part 3)

Introducing convolutional neural networks: In part three of Learn Machine Learning Zero to Hero, AI Advocate Laurence Moroney (lmoroney@) discusses convolutional Neural Networks and why they are so powerful in Computer vision scenarios. A convolution is a filter that passes over an image, processes it, and extracts features that show a commonality in the image. In this video you’ll see how they work, by processing an image to see if you can extract features from it!

Codelab: Introduction to Convolutions → http://bit.ly/2lGoC5f

Build an image classifier (ML Zero to Hero, part 4)

Build an image classifier: In part four of Learn Machine Learning Zero to Hero, AI Advocate Laurence Moroney (lmoroney@) discusses the build of an *image classifier *for rock, paper, and scissors. In episode one, we showed a scenario of rock, paper, and scissors; and discussed how difficult it might be to write code to detect and classify these. As the episodes have progressed into Machine Learning, we’ve learned how to build neural networks from detecting patterns in raw pixels, to classifying them, to detecting features using convolutions. In this episode, we have put all the information from the first three parts of the series into one.

Colab notebook: http://bit.ly/2lXXdw5

Rock, paper, scissors dataset: http://bit.ly/2kbV92O

Thanks for reading

If you liked this post, share it with all of your programming buddies!

Follow us on Facebook | Twitter

#machine-learning #tensorflow #python #data-science #artificial-intelligence

Learn Machine Learning Zero to Hero!
1 Likes133.60 GEEK