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ComputerUncategorized
Home›Computer›AI courses by GOOGLE

AI courses by GOOGLE

By fazilmuhammed
04/25/2018
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Google main promoting AI in all fields Google car is the eg of AI. The main AIM of Google is promothing AI,because of that GOOGLE started some courses for students some of them explain here.

Google.ai is a division of Google dedicated solely to artificial intelligence. It was announced at GoogleI/O 2017 by CEO Sundar Pichai. Educational resources from machine learning experts at Google.

Machine Learning Crash Course.

Machine Learning Crash Course does not presume or require any prior knowledge in machine learning. However, to understand the concepts presented and complete the exercises, we recommend that students meet the following prerequisites:

  • Mastery of intro-level algebra.You should be comfortable with 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.)
  • Proficiency in programming basics, and some experience coding in Python.Programming exercises in Machine Learning Crash Course are coded in Python using TensorFlow. No prior experience with TensorFlow is required, but you should feel comfortable reading and writing Python code that contains basic programming constructs, such as function definitions/invocations, lists and dicts, loops, and conditional expressions.

https://developers.google.com/machine-learning/crash-course/prereqs-and-prework

TensorFlow Workshops

TensorFlow Workshops

Exercises for use at events.

How-to run these notebooks

To run these notebooks, you’ll need to:

  1. Install TensorFlow
  2. Install Jupyter
  3. Clone this repo
  4. Start Jupyter, and open a notebook

Machine Learning Glossary

his glossary defines general machine learning terms as well as terms specific to TensorFlow.

A

A/B testing

A statistical way of comparing two (or more) techniques, typically an incumbent against a new rival. A/B testing aims to determine not only which technique performs better but also to understand whether the difference is statistically significant. A/B testing usually considers only two techniques using one measurement, but it can be applied to any finite number of techniques and measures.

https://developers.google.com/machine-learning/glossary/?utm_source=google-ai&utm_medium=card-image&utm_campaign=training-hub&utm_content=ml-glossary

The 7 Steps of Machine Learning

https://www.youtube.com/watch?v=nKW8Ndu7Mjw&list=PLIivdWyY5sqJxnwJhe3etaK7utrBiPBQ2

other courses offered by google

https://ai.google/education/#?modal_active=none

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