|Google Provides Free Machine Learning For All|
|Written by Sue Gee|
|Wednesday, 07 March 2018|
Google's Machine Learning Crash Course has already been delivered to more than 18,000 Googlers and now it it have been made available for free and to all as part of Learn with Google AI, a new educational resource aimed at every developer.
Google AI is Google's portal for everything to do with Artificial Intelligence. In its Our Work section you'll find a selection of headlining research using AI plus use of AI in Google Translate and Google's forays into mobile photography together with a large selection of machine learning projects. Google has three distinct AI labs, Google Research, Brain Team and PAIR (People+AI Research Initiative) and there are links to explore the work of all three. In the Tools section you'll find links to Tensorflow, Cloud TPUs, Cloud Machine Learning and Google's recent acquisition Kaggle, which describes itself as:
The Home of Data Science & Machine Learning
Zuri Kemp who leads Google’s machine learning education effort with the aim of making AI and its benefits accessible to everyone, announced the new Education section saying:
To help everyone understand how AI can solve challenging problems, we’ve created a resource called Learn with Google AI. This site provides ways to learn about core ML concepts, develop and hone your ML skills, and apply ML to real-world problems. From deep learning experts looking for advanced tutorials and materials on TensorFlow, to “curious cats” who want to take their first steps with AI, anyone looking for educational content from ML experts at Google can find it here.
Occupying the top slot in the list of resources comes the Machine Learning Crash Course (MLCC), a free course that provides exercises, interactive visualizations, and instructional videos that anyone can use to learn and practice ML concepts. According to this visual overview it provides 15 hours of material divided into 25 lessons with a variety of materials.
Introducing MLCC on the Google Developer's blog, Barry Rosenberg of the Google Engineering Education Team explains:
Our engineering education team originally developed this fast-paced, practical introduction to ML fundamentals for Googlers. So far, more than 18,000 Googlers have enrolled in MLCC, applying lessons from the course to enhance camera calibration for Daydream devices, build virtual reality for Google Earth, and improve streaming quality at YouTube. MLCC's success at Google inspired us to make it available to everyone.
MLCC covers many machine learning fundamentals, starting with loss and gradient descent, then building through classification models and neural nets. The programming exercises introduce TensorFlow. You'll watch brief videos from Google machine learning experts, read short text lessons, and play with educational gadgets devised by instructional designers and engineers.
This introductory video for the course comes from Peter Norvig and indicates what to expect:
While MLCC doesn't require any prior knowledge in machine learning, to understand the concepts presented and complete the exercises, it is recommended that students have some experience coding in Python in order to tackle its TensorFlow programming exercises and with intro-level algebra - variables and coefficients, linear equations, graphs of functions, and histograms; familiarity with more advanced math concepts such as logarithms and derivatives is helpful, but not required.
Learn with Google AI also has Machine Learning video how-to's, sample code, documentation including a glossary, links to TensorFlow on GitHub, links to courses on Udacity on Coursera and access to Kaggle Learn. To help you find the resources you need to can filter by type of content, your stage of ML development and your role - researcher, data scientist, software engineer, business decision maker, student or "curious cat".
According to Zuri Kemp, more resources will be added including more courses and documentation. It seems to be shaping up to be a great resource for getting into Machine Learning with TensorFlow.
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|Last Updated ( Wednesday, 07 March 2018 )|