Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning

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Most people think Artificial Intelligence and Machine Learning are the same. However, it’s wrong because there are some differences between these two. Nevertheless, it’s an application of AI.  As I mentioned in my previous articles, simply we can define that it is human-made thinking power. Instead of pre-programming, it is used such algorithms which work with their own thinking power. Currently, we are working with AI and it will be more affected human lives more than now. Let’s look at Artificial Intelligence and Machine Learning.

Machine Learning

Machine learning is it provides systems the ability to learn and improve automatically 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 main target of ML is to allow computers to learn automatically without human assistance. Machine Learning works only for some specific tasks. If we enter some data other than what we have given to the system, it will be irresponsive.

Machine Learning can be divided into 3 parts.

  •  Supervised Learning – (Examples-: Predictive Analytics, Image and object recognition, Spam detection)                      
  • Reinforcement Learning
  • Unsupervised Learning

Machine Learning is being used in an E-mail spam filter, Facebook auto friend tagging system, Google search algorithms, online recommender system, etc.

Let’s consider some differences between AI and ML……………

In Artificial Intelligence, intelligent systems are made to perform many tasks as a human but in ML, machines are taught with data to perform a particular task and give an accurate result. The main subset of Machine Learning is deep learning. AI has machine learning as a subset in addition to deep learning. Deep learning is also known as deep Neural Network or Deep Neural Learning. It is designed to follow how humans learn and think.  Even though AI has a wide range of scope, Machine Learning has a limited scope. Machine Learning always deals with structured and semi-structured data but AI completely deals with structured, semi-structured, and unstructured data. While machine learning considers accuracy and patterns, AI is concerned about maximizing the chances.

Advantages and disadvantages of machine learning

As we all know every field has two sides like advantages and disadvantages. So has ML.

Advantages of Machine Learning

With ML, you don’t need to give all instructions step by step. The machine is given the ability to learn and it will make predictions by itself. ML algorithms are good at handling multi-dimensional and multi-variety data. If we are using some e-commerce websites, they can identify according to our past search history what we are looking for and what we wish to buy. So always they try to show us the relevant advertisements and deals in order to make their work much easier.

Disadvantages of Machine Learning

Some factors that let us not to tell ML is perfect.

ML needs much time to let the algorithms learn in order to get more accurate results. Not only much time it needs more rescores to function. We should choose algorithms carefully because then the results generated by algorithms can be clarified accurately. Machine learning needs massive data sets and these data should be good in quality. Sometimes they have to wait till the new data to be generated. Though it is an autonomous highly vulnerable to errors.

For more-

What is Machine Learning? | IBM

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