Over time, science has evolved into something marvelous. Computer Science has progressed to such a degree that artificial intelligence has now become the foundation of every technological task. Artificial Intelligence and Machine Learning can go hand in hand and can also be interdependent.

MEANING:

Machine learning is defined as a subset or application of Artificial Intelligence [AI] that grants computer systems the ability to learn, improve and effectively perform instructions and tasks based on past experiences while relying widely on inferences and patterns. These algorithms help systems to perform tasks without prior explicit instructions being given. The development of these computer programs ensures that they can access data independently and take further action themselves.

In layman’s terms, human beings learn from past experiences and then take steps into the future. This thinking has led humans to produce algorithms which help machines act independently from past experiences. This can save a lot of time when processing data.

TYPES:

Machine Learning Algorithms are characterized as either supervised or unsupervised.

  • Supervised Algorithms

Supervised Algorithms apply what has been learned in the past to new data. It uses past examples to predict future events accordingly. The algorithm’s output can then be compared to the intended output. Errors can be found and corrected. This results in the appropriate modification of the model. 

  • Unsupervised Algorithms

Unsupervised Algorithms are used when the information used machine training is neither classified nor labeled. This analyzed data draw inferences from the datasets. 

APPLICATION:

Machine Learning has various applications in life that can be used to solve human problems. It is used in many sectors.

The sectors are as follows:

Agriculture: Precision agriculture and satellite farming are concepts based on defining a Decision Support System [DDS] which helps maximize output while saving resources. For this purpose, Machine Learning is employed.

Economics: Computational Economics uses a computer-based economic model to calculate complex statistics and provide results related to economic mathematical problems. It uses software to solve matrix problems as seen in a matrix inverse calculation or when solving linear and non-linear equations.

Medical Diagnosis: To assist doctors and to help in diagnosing ailments, a Computer Aided Diagnosis [CADx] or Computer Aided Detection [CADe] is used. These help doctors interpret various medical images like X-ray, ultrasounds and MRIs, which always contain a great deal of information that radiologists have to interpret.

Search Engines: a Web Search Engine is a software system that aims to search the World Wide Web for results in an organized manner expressed in the web search query.

DIFFERENCE BETWEEN AI AND MACHINE LEARNING

Artificial Intelligence does not have to be trained. It can take action independently and provide a   personalized reaction.

Machine Learning needs to be trained. It needs to be told what is right and what is wrong so it can take action accordingly. 

CONCLUSION:

Artificial Intelligence and Machine Learning have been present for a long time, but they have recently been made more accessible by the youth of today. In the past, it would take a large amount of computing power to use a single algorithm, but now even the common man can access it. Algorithms exist that can tell you whether someone you are taking a photograph of is a male or female.  It can tell you what his/her approximate age is based on physical characteristics that it receives and then includes in their calculations. This requires an immense compilation of statistics. Hence, statistics then become an important factor in the function of these algorithms. These algorithms help perform tasks with ease, perfection and speed.

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