Date: Thursday, August 11th, 2022

Google machine learning certification its uses

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Google machine learning certification
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Google, like many other businesses these days, uses machine learning (ML) to make day-to-day business decisions, predict customer behaviours, and reap plenty of benefits that come with that. So, it guides for this skill Google has its own “Google machine learning certification”.

So, Google offers the Machine Learning Certifications for two of its most popular ML frameworks – TensorFlow and TensorFlow Lite. Also, These certifications will teach you how to develop applications using the various ML approaches offered by Google.

This article aims at guiding you comprehensively through the process of clearing the Google Machine Learning Certification Exam – by providing answers to all the important questions about it. We’ll also provide you with pointers on ways to study for the exam and some miscellaneous tips to ace it with flying colours.”

Machine Learning – The Basics

So, Machine learning is a small part of artificial intelligence that includes the development of computers that can read without being explicitly programmed. Also, These learning machines are capable of making predictions, making decisions, and learning from past experiences.

Artificial Intelligence (AI) has been a very hot topic in the recent past. This has directed to a growth in demand for AI specialists. It is expected that by 2020, AI experts will make up around one-third of the global IT workforce.

Google has a history of investing in AI and machine learning. In 2010, Google acquired DeepMind, a UK-based AI firm, for a reported $500 million. This acquisition paved the way for the creation of Google Brain, which has focused on developing state-of-the-art AI algorithms and solutions for Google’s products.

So, the Google Brain now boasts of the most advanced deep neural networks in the world. Google Brain was one of the founding members of the Allen Institute for Artificial Intelligence (AI2), a $750 million research organization that was launched in 2014.

Google Brain, together with IBM and Microsoft, are developing AI. Today, many companies are already employing machine learning and AI to make their day-to-day business decisions, predict customer behaviours, and reap plenty of benefits that come with that.

Google, like many other businesses these days, uses machine learning (ML) to make day-to-day business decisions, predict customer behaviours, and reap plenty of benefits that come with that.

The different types of machine learning

Machine learning is very broad and encompasses a variety of sub-categories. These sub-categories are based on the kind of problems you’re trying to solve.

The different sub-categories of machine learning are as follows:

  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Reinforcement Learning
  • Transfer Learning
  • Active Learning
  • Unsupervised Deep Learning

Supervised Machine Learning

In supervised machine learning, you train your machine learning models by feeding them a large dataset of training examples. These examples are classified based on the kind of output you want your model to give.

For example, you may be trying to classify the colour of a picture. To do this, you’ll need a model that’s capable of predicting the colour of an image. The training examples are the images you want your model to predict the colour of. The model will then make predictions based on the training examples and will then classify the images based on these predictions.

Deep Learning

So, Machine learning has reached a long way following its early origins. It is a sub-category of all these machine learning branches. Deep learning uses neural networks to classify data and make predictions. Neural networks are a type of artificial intelligence that is capable of simulating the human brain.

Neural networks have many layers. Each layer has a number of neurons. The neurons in each layer are linked to the neurons in the next layer.

Neural networks are very good at solving problems that require pattern recognition. For example, you may want to know if a certain picture is a dog or a cat. If you feed your neural network a picture of a dog, it will be able to recognise it like a dog.

So, You can even teach your neural network to recognise the colour of a cat or a dog. It will be able to learn from the images you feed it.

Also, You can use supervised machine learning to classify images based on their colour. You will need to use an unsupervised machine learning technique to create your model.

Unsupervised Deep Learning

Unsupervised deep learning is a very powerful technique that you can use to classify images.

This is one of the most effective techniques for image classification. In this technique, you don’t use training examples to train your model.

Instead, you create a model by using a set of images and feeding it a set of parameters. The parameters are also called hyper-parameters. These parameters are usually numbers.

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Google’s Machine Learning Research

Moreover, The team has created a Python package called Keras that makes it very easy to create models using deep learning. Also, You can create a deep learning model using a graphical user interface that looks like this. After you have trained your model, you can use it to classify images.

Reinforcement Learning

Moreover, Reinforcement learning is a technique that you can use to solve certain problems. For example, you may be trying to build a self-driving car. To do this, you need a model that can learn to drive by itself.

You train your model by playing a game against it. In this game, you want the model to do what you want it to do. For example, you may want the model to avoid crashing.

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Conclusion

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David Smith

David Smith

I am a Social media and Google products content writer. Technology press release and product launching special author at SBtechpoint.
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