Understanding Machine Learning

Big Data and Machine Learning, You hear these words all over the place without really understanding what is going on behind them. However, tech knows you. But you may never have really tried to understand precisely how machine learning (or “machine learning”) works because that is not your specialty. Since it is not forbidden to learn about artificial intelligence technologies, whatever our profession, here is our article, which will hopefully help you to understand the principle of Machine Learning and to understand its close link with Big Data.

What is Machine Learning, or “Machine Learning”?

In itself, Machine Learning is not new. But we have only started to take a close interest in it for a few years because the possibilities it offers are now colossal. Its definition is not, as such, particularly complex: it is a technology for making predictions. For this, it is based on data mining, pattern recognition, statistics, and predictive analyzes. The best known of the Machine Learning algorithms is Perceptron.

Many wonder what exactly is the connection between Big Data and Machine Learning. The main thing to understand is that machine learning technology is particularly effective in finding solutions from numerous, diverse, and changing data that only big data can store.

The benefits of using Machine Learning with Big Data

To fully understand the interest of Machine Learning, it is still necessary to identify that of Big Data. Another word used wrongly and begins to annoy some, to frighten others, that everyone knows, but that ultimately very few people understand.

So what is Big Data? The term literally means Big Data or Big Data. It will be understood that it is therefore used to manage and analyze very large volumes of data. We cannot give a more precise definition — and this is probably the reason why it remains so vague — because its use and utility depend entirely on the market players who design or use it.

To harness the value of Big Data by seeking to understand and analyze data, traditional analytical tools are not powerful enough: the volume of data is so large that it is too difficult to test all the assumptions and make them.

We explain in more detail Big Data.

Business intelligence (BI) and reporting tools simply allow you to do accounts and perform SQL queries. Their analytical processing requires the intervention of a human in order to specify what should be calculated.

Machine Learning makes it possible to extract the value of this data without human intervention. Unlike basic analysis tools, this one is ideal for analyzing both complex and massive data. With machine learning, the more data, the more the system can learn, progress, and find quality results . This is precisely why we talk about machine learning.

Machine Learning is therefore increasingly popular, especially in schools where specialized masters are on the rise. Python is one of the best languages ​​to start machine learning. The R language is also widely used. Both have advantages and disadvantages: Python is easier to learn, and better suited to data manipulation and repetitive tasks. R, on the other hand, is more suited to projects heavy in statistics and for occasional exploration of datasets. But R can only be used in Big Data, while Python can also be used in web development. If you are a web developer and you are interested in artificial intelligence, it may be interesting to get into Python slowly.

Can we do Machine Learning without Big Data?

And the answer is… no. But if we have read correctly so far, we will have understood!

The whole point of machine learning is to allow the system to become more and more intelligent. To do this, it needs to be “fed” with an increasing amount of data, in order to analyze more and more. Big Data is, therefore the essential tool for Machine Learning and all artificial intelligence (AI) technologies.

Today, they can learn without the help of a human and access a colossal volume of data in real-time. The analysis is done almost instantaneously, as the AI ​​has become agile.

Some use cases of Machine Learning.

It is always easier to understand the interest of techno with a few examples of applications.

A first example of the interest of Machine Learning is the facilitation of fraud detection. Every change in consumer behavior can now be easily analyzed, and bank fraud is now very quickly detected by algorithms.

In e-commerce, Machine Learning is valuable for retargeting: depending on the history of products viewed, purchased, and queries typed by the Internet user, purchase recommendations will allow the customer to be targeted with products. that interest him. Netflix uses this technique to recommend movies that match the cinematic tastes of its customers. Amazon is also increasing this sale tenfold thanks to ultra-targeted targeting.

What about Deep Learning?

We often hear about Deep Learning, some wondering the difference between Machine Learning and Deep Learning. The two do not have to be distinguished since Deep Learning is actually a subcategory of Machine Learning. Deep Learning, for example, concerns everything related to visual or voice recognition. It will be particularly useful in the event of a police investigation to recognize a face on a surveillance camera, but also for more common cases, such as to develop conversational chatbots (Siri, Alexa, etc.).

Deep Learning seeks to reproduce a neural network such as this one that exists in nature: very often, it is a question of seeking to reproduce human behavior. The learning is supervised here: we will define what we want to teach the robot. If you are a fan of the Westworld series, we are right in it, since the robots are created in the image of humans and respect (at the beginning of the series, in any case…) a specifically written scenario. For the robot to learn to improvise and know how to feed on new information from its observations, we will need much higher computing power.

You now have the basics of Machine Learning. The reality is, of course, much more complex, and you can diversify your research on Lebigdata.fr or Bial-r. On OpenClassrooms, you can get your hands dirty a bit more.

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