What is machine learning: an introduction By Codding Ninjas.
In this series of articles, we work towards deep learning. To give a clear picture of deep learning, we first discuss machine learning and neural networks. To clearly explain deep learning, it is important to outline the connections between all these concepts. After the brief introduction in the previous article, we will now discuss these topics in more detail. We start with machine learning. We provide a definition in this introduction. Get to know about Online Machine learning Course In India?
What is machine learning: a definition
Machine learning is a broad field of research within AI that deals with the development of algorithms and techniques that computers can use to learn. Machine learning refers to any system where a machine's performance when performing a task is improved by gaining more experience in performing that task. Machine learning, therefore, consists of algorithms that learn thanks to data. It is about using statistical/mathematical techniques to enable computers to learn without being explicitly programmed. With the help of iterative data learning algorithms, machine learning can find hidden insights without being explicitly programmed to look where.
What is machine learning: task T, experience E and performance measure P
A commonly used definition of machine learning is: a computer program is said to learn from experience E with regard to a certain task class T and performance measure P as its performance on tasks in T, as measured by P, improves with experience E. So if you want that your program makes predictions, for example, traffic patterns at a busy intersection (task T), you can execute it through a machine learning algorithm with data on historical traffic patterns (experience E) and, if it has successfully learned, it will perform better when predicting future traffic patterns (performance measure P).
What is machine learning: learning experience
If machine learning models are exposed to new data, they can adjust independently. They learn from previous calculations to produce reliable, repeatable decisions and results. Important to understand that the 'learning' effect is basically two-fold: learning data (known observations) and learning new events (new observations). The latter is really about learning experiences. The aim is to automate these decisions and predictions as much as possible on the basis of self-learning algorithms (without human intervention).
What is machine learning: renewed attention thanks to Big Data
Machine learning is part of Artificial Intelligence and is logically often used in the development of AI applications, such as Apple's Siri for speech recognition. Given the attention to the concept, you would almost think that it is something new. However, the first algorithms were used 50 years ago. But what makes machine learning so interesting right now is that the world around us has changed: the digital age has led to an explosion of data in all forms and from all regions of the world.
This data, known as Big Data, comes from sources such as social media, internet search engines, e-commerce platforms, online cinemas, etc. This huge amount of data is easily accessible and can be shared via applications such as cloud computing. However, the amount of data that is normally unstructured is so large that it can take decades for people to understand and extract relevant information.
See also: Practice coding online for a better chance to crack that interview
What is machine learning: a definition
Machine learning is a broad field of research within AI that deals with the development of algorithms and techniques that computers can use to learn. Machine learning refers to any system where a machine's performance when performing a task is improved by gaining more experience in performing that task. Machine learning, therefore, consists of algorithms that learn thanks to data. It is about using statistical/mathematical techniques to enable computers to learn without being explicitly programmed. With the help of iterative data learning algorithms, machine learning can find hidden insights without being explicitly programmed to look where.
What is machine learning: task T, experience E and performance measure P
A commonly used definition of machine learning is: a computer program is said to learn from experience E with regard to a certain task class T and performance measure P as its performance on tasks in T, as measured by P, improves with experience E. So if you want that your program makes predictions, for example, traffic patterns at a busy intersection (task T), you can execute it through a machine learning algorithm with data on historical traffic patterns (experience E) and, if it has successfully learned, it will perform better when predicting future traffic patterns (performance measure P).
What is machine learning: learning experience
If machine learning models are exposed to new data, they can adjust independently. They learn from previous calculations to produce reliable, repeatable decisions and results. Important to understand that the 'learning' effect is basically two-fold: learning data (known observations) and learning new events (new observations). The latter is really about learning experiences. The aim is to automate these decisions and predictions as much as possible on the basis of self-learning algorithms (without human intervention).
What is machine learning: renewed attention thanks to Big Data
Machine learning is part of Artificial Intelligence and is logically often used in the development of AI applications, such as Apple's Siri for speech recognition. Given the attention to the concept, you would almost think that it is something new. However, the first algorithms were used 50 years ago. But what makes machine learning so interesting right now is that the world around us has changed: the digital age has led to an explosion of data in all forms and from all regions of the world.
This data, known as Big Data, comes from sources such as social media, internet search engines, e-commerce platforms, online cinemas, etc. This huge amount of data is easily accessible and can be shared via applications such as cloud computing. However, the amount of data that is normally unstructured is so large that it can take decades for people to understand and extract relevant information.
See also: Practice coding online for a better chance to crack that interview
Those who provide education for machine learning should get inspiration from the course module. Machine learning solution providers Click here to learn more about machine learning companies.
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