Machines vs Humans
One of the main differences between humans and computers is that humans learn from past experiences, at least they try, but computers or machines need to be told what to do. Computers are strict logic machines with zero common sense.
That means if we want them to do something, we have to provide them with detailed, step-by-step instructions on exactly what to do.
So we write scripts and programmed computers to follow those instructions. That’s where Machine Learning comes in. Machine learning Concept consists of getting computers to learn from experiences-past data.
What is Machine Learning?
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
Machine learning methods
Machine learning algorithms are often categorized as supervised or unsupervised.
Supervised Machine Learning algorithms
It can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values.
Unsupervised Machine Learning algorithms
In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data.
Semi-supervised Machine Learning algorithms
Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning since they use both labeled and unlabeled data for training — typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy.
Reinforcement machine learning algorithms
Reinforcement machine learning algorithm is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance.
Examples of Machine Learning
Machine learning is being used in a wide range of applications today. One of the most well-known examples is Facebook’s News Feed. The News Feed uses machine learning to personalize each member’s feed. If a member frequently stops scrolling to read or like a particular friend’s posts, the News Feed will start to show more of that friend’s activity earlier in the feed.
Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in the user’s data and use those patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend’s posts, that new data will be included in the data set and the News Feed will adjust accordingly.
Machine learning is also entering an array of enterprise applications. Customer relationship management (CRM) systems use learning models to analyze email and prompt sales team members to respond to the most important messages first.
More advanced systems can even recommend potentially effective responses. Business intelligence (BI) and analytics vendors use machine learning in their software to help users automatically identify potentially important data points.
Human resource (HR) systems use learning models to identify characteristics of effective employees and rely on this knowledge to find the best applicants for open positions.
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