We currently live in the data era, in which a large amount of it is collected and stored every day.
In the time this article was written:
• There are 4,156,513,325 users on the Internet, it would take more than 128 years to count them.
• 1,755,606,975 websites on the Internet.
• 168.673.726.872 e-mails sent only today.
• 4,723,747,823 Google searches
• 4,033,234 blog posts
• 4,553,543,234 YouTube video views today only.
This is a lot of data to manage, even for computers.
Machine Learning is a branch of Artificial Intelligence (AI) that offers computers the possibility of learning without being explicitly programmed.
In the field of computer science, machine learning is a variation of traditional programming in which a machine has the ability to learn something from the data independently, without receiving explicit instructions about it, in essence, learns from experience.
ML is a field of study that exploits the principles of computer science, automation and statistics to create statistical models and to further improve the performance of an algorithm in identifying patterns in the data
These models are generally used to do two things:
1. Forecasting: predicting the future based on past data
2. Inference: discovering patterns in the data
Difference between ML and AI: there is no universal agreement on the distinction between ML and artificial intelligence (AI). Artificial intelligence usually concentrates on programming computer to make decisions (based on ML models and sets of logical rules), whereas ML focuses more on predicting the future.
They are highly interconnected fields and, for most non-technical purposes, are the same.
The rudimentary algorithm with which every Machine Learning logoic starts is a linear regression algorithm.
Regression is a method of modeling a target value based on independent predictors. This method is mainly used for predicting and researching the cause and effect relationship between variables. Regression techniques differ mainly based on the number of independent variables and the type of relationship between independent and dependent variables.
New technologies have forced companies to change the way they interact with their customers.
Machine learning is also used to have very detailed data on its customers, in order to be able to meet their needs in the best possible way.
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People from different disciplines are trying to apply AI to make their tasks easier and more efficient. For example, economists use AI to predict future market prices to make a profit, doctors use AI to classify whether a tumor is malignant or benign, meteorologists use AI to predict the weather, recruiters human resources use AI to check the candidates’ resume to see if the applicant meets the minimum criteria for the job, etc.
One of the ways to use machine learning is to improve the online shopping experience, personalizing it as much as possible.
Sales processes can be easily automated through chatbots that act as if they were human beings, guiding the customer and giving advice.
An example is Netflix, which recommends TV series and films based on what has already been seen, or the use of chatbots that interact with the potential customer as if they were human beings.
Healthcare
AI applications can provide personalized medical and radiographic readings. Personal health care workers can act as life coaches, reminding you to take pills, exercise or eat healthier.
Manufacturing
AI is able to analyze corporate IoT data while streaming from connected equipment to predict expected load and demand using recurring networks, a specific type of deep learning network used with sequence data.
Retail
AI offers virtual shopping features that offer personalized recommendations or present the different purchase options to the consumer. Technologies for inventory management and site configuration will also be improved with AI.
Sports
In this field, AI can be used to capture and analyze game images, provide coaches with reports on how to better organize a team, for example, including optimizing positions on the pitch and strategy.
The most common languages and frameworks used nowadays or software development with regards to Machine Learning are Python and R with the support of HTML as browser.
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Another advantage in the use of machine learning is also that of being able to have more and more updated information on existing customers and potentials. With machine learning, it will always be easier to have a list of potential customers knowing already how to interact with them, as you will already have all the necessary data.
Taken as a whole, Machine Learning can have multiple uses and can be very useful if combined with a strategy that aims to optimize the company on all levels.