Understand Machine Learning What is Machine Learning ?

For example, an algorithm can learn the rules of a certain language and be tasked with creating or editing written content, such as descriptions of products or news articles that will be posted to a company’s blog or social media. On the other hand, the use of automated chatbots has become more common in Customer Service all around the world. These chatbots can use Machine Learning to create better and more accurate replies to the customer’s demands. The training of machines to learn from data and improve over time has enabled organizations to automate routine tasks that were previously done by humans — in principle, freeing us up for more creative and strategic work.

But in reality, you will have to consider hundreds of parameters and a broad set of learning data to solve a machine learning problem. If you want to learn more about how this technology works, we invite you to read our complete autonomous artificial intelligence guide or contact us directly to show you what autonomous AI can do for your business. Because Machine Learning learns from past experiences, and the more information we provide it, the more efficient it becomes, we must supervise the processes it performs. It is essential to understand that ML is a tool that works with humans and that the data projected by the system must be reviewed and approved. It works through an agent placed in an unknown environment, which determines the actions to be taken through trial and error. Its objective is to maximize a previously established reward signal, learning from past experiences until it can perform the task effectively and autonomously.

Logistic Regression

Human experts determine the set of features to understand the differences between data inputs, usually requiring more structured data to learn. The famous “Turing Test” was created in 1950 by Alan Turing, which would ascertain whether computers had real intelligence. It has to make a human believe that it is not a computer but a human instead, to get through the test. Arthur Samuel developed the first computer program that could learn as it played the game of checkers in the year 1952. The first neural network, called the perceptron was designed by Frank Rosenblatt in the year 1957. It also allows continuous monitoring of production models for accuracy and relevance, with auto-triggered alerts in the event of model and data drift.

In the last few years, especially thanks to the recent advancements in the field of Deep Learning, Machine Learning has drawn a lot of attention. One of the main driving factors of the machine learning hype is related to the fact that a unified framework for introducing intelligent decision-making into many domains. In the following chapters, we will introduce examples of possible applications of machine learning to networking scenarios.

Real-World Examples of Machine Learning (ML)

Strong AI can only be achieved with machine learning (ML) to help machines understand as humans do. The main difference with machine learning is that just like statistical models, the goal is to understand the structure of the data – fit theoretical distributions to the data that are well understood. So, with statistical models there is a theory behind the model that is mathematically proven, but this requires that data meets certain strong assumptions too. Machine learning has developed based on the ability to use computers to probe the data for structure, even if we do not have a theory of what that structure looks like. The test for a machine learning model is a validation error on new data, not a theoretical test that proves a null hypothesis.

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