Machine Learning Nuts and Bolts, Wheels and Gears: The Video

Getting down to the nitty-gritty, it’s time to take a practical view of what is involved in setting up Machine Learning (ML) projects. There is a lot of material available, but it can be difficult to find accessible information that can guide you through the development and implementation maze. Here we look at how to access ML APIs, how to use TensorFlow, and common algorithms that you might wish to use for various purposes.

A more practical understanding gives you an edge in discussing possbilities, as also heading you toward the right track if you wish to add these skills to your programming arsenal.

The videos are a mixture of talks, presentations and teaching material available under standard YouTube license, with landing page descriptions added. Minor edits have been made where necessary.

Basic Machine Learning Algorithms Overview – Data Science Crash Course Mini-series (Hortonworks)

Published on Aug 1, 2017

A high-level overview of common, basic Machine Learning algorithms by Robert Hryniewicz

Hello World – Machine Learning Recipes (Google Developers)

Published on Mar 30, 2016

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.

Deep Learning, Self-Taught Learning and Unsupervised Feature Learning (Stanford Graduate School of Business)

Published on May 13, 2013

Talk by Andrew Ng.

TensorFlow and Deep Learning without a PhD, Part 1 (Google Cloud)

Published on Mar 8, 2017

With TensorFlow, deep machine learning transitions from an area of research to mainstream software engineering. In this video, Martin Gorner demonstrates how to construct and train a neural network that recognises handwritten digits. Along the way, he’ll describe some “tricks of the trade” used in neural network design, and finally, he’ll bring the recognition accuracy of his model above 99%.

Content applies to software developers of all levels. Experienced machine learning enthusiasts, this video will introduce you to TensorFlow through well known models such as dense and convolutional networks. This is an intense technical video designed to help beginners in machine learning ramp up quickly.

TensorFlow and Deep Learning without a PhD, Part 2 

(Google Cloud)

Published on Mar 10, 2017

Deep learning has already revolutionized machine learning research, but it hasn’t been broadly accessible to many developers. In this video, Martin Gorner explores the possibilities of recurrent neural networks by building a language model in TensorFlow. What this model can do will impress you.

Developers with no prior machine learning experience are welcome. We do recommend that you watch the previous video unless you already know about dense and convolutional networks and are only interested in recurrent networks.

This is an intense technical video designed to help beginners in machine learning ramp up quickly.


 

 


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