What Is AI and Machine Learning? How It Works, Its Type, AI and ML Courses

internet and technology

What Is AI and Machine Learning?

AI & Machine learning images
AI & Machine learning

    Today if humans have developed so much in the field of technology, then our human brain has the biggest hand in it. On the strength of this intelligence, humans have done many inventions and there is nothing to tell that every invention has given a new direction to the life of human beings. When computers were made, no one thought that we would be able to use anything like a smartphone in the future.
    But today it is not only a part of our life but also helps a lot in any of our work. In the last few years, in order to take technology to a different level, some scientists of computer science had put AI concepts in front of the world. Its basic objective was to create such computer-controlled robots or software that can solve a problem by thinking like humans.
    But many other scientists believe that such development in technology can make machines super-intelligent which will further threaten human existence. How beneficial will Artificial Intelligence or Machine Learning be for humans, it will tell the future. Let’s discuss what is Artificial intelligence (AI)?

    What is Artificial Intelligence

    Artificial Intelligence (AI)  is a branch of computer science that is developing machines that can think and act like humans. Such as- Voice Recognition, Problem Solving, Learning, and Planning. It is the intelligence showed by machines as opposed to the generic intelligence displayed by humans and animals.

    Artificial Intelligence image
    Artificial Intelligence

    It plans to create a computer-controlled robot or software that can think in the same way as the human mind thinks. Artificial intelligence is repeatedly being prepared to make it perfect. In its training it is taught experience from machines, is prepared to keep pace with new inputs and perform human-like tasks.

    So overall, with the use of Artificial Intelligence, such machines are being created, which can interact with their environment and work wisely on the data received. That is, if AI is the concept in the future is stronger, then it will be like our friend. If you get a problem, then you will tell yourself what to do for a specific problem.

    Artificial Intelligence (AI) is basically the ability to think and learn about a machine or computer program. This concept is based on the idea that machines should be made capable enough to think about a problem like humans and work on it and learn from it. In some cases, AI is a more powerful and good thinking capability program that is better than a normal human brain. In future AI becomes the most important and time-saving functionality for every sector like Organization, Industries, IT companies, Restaurants &  CafĂ© shops, etc.

    History of AI

    1950 was the year when Artificial intelligence research started. Research in the field of AI began with the development of electronic computers and stored-program computers. Even after this, for many decades, a link could not connect a computer to think or act like a human mind. Later, a discovery that greatly accelerated the early development of AI was made by Norbert Wiener. You can search for Norbert Wiener’s AI research in Google.

    He has proved that all intelligent behavior of human beings is the result of the reaction mechanism. Another step in the direction of modern AI was when Logic Theorist was created. Designed by Newell and Simon in 1955, it is often considered the primary AI program.

    Who Invent AI? 

    After much research, the person who eventually laid the foundation of Artificial intelligence was the father of AI, John McCarthy, an American scientist. In 1956, he organized a conference “The Dartmouth Summer Research Project on Artificial Intelligence” to further develop AI. In which all those people who were interested in machine intelligence could participate. The purpose of this conference was to attract the talent and expertise of interested people to help McCarthy in this task.

    In later years the AI Research Center was formed at Carnegie Mellon University as well as Massachusetts Institute of Technology. Along with this, AI also faced many challenges. The first challenge they faced was to build a system that could solve a problem efficiently with very little research. The second challenge is building a system that can learn a task by itself. The first breakthrough in the field of artificial intelligence came when a Novel program called General Problem Solver (G.P.S) was created by Newell and Simon in 1957.

    This was an extension of Wiener’s feedback theory. Through this, the problems of general knowledge could be solved as much as possible. LISP language was created in 1958 by John McCarthy in AI History. It was soon adopted by many AI researchers and remains in use today.

    What is Artificial Techniques

    AI technique is a method that uses derived knowledge. So that its error can be modified to correct it. 
    AI the technique is an advanced model of statistical and mathematical models. These models make it realizable for a computer or machine to evaluate the tasks that are executed by humans. Some examples of this are:
    1. Artificial Natural Network
    2. Heuristics Search
    3. Markov Decision Process
    4. Natural Language Modeling.

    Characteristics Of AI

    In this era of technology, AI has started to rule all industries and many fields. The biggest reason for this is that the machine works more effectively than humans. So the day is not far when robots will dominate our world like any Hollywood movie.
    AI is divided into two main Categories.

    First Category:-

    Weak Intelligence (Week AI) – 

    Weak intelligence, also known as Narrow AI, focuses entirely on the functioning of the Small task. Weak AI is meant to meet a particular problem as opposed to a strong AI or general artificial intelligence. This machine is not very smart in doing its work. But they are made to look smart. For example, when playing Chess on the computer, when you move your pieces then after the computer understands your move and plays like a human to avoid the defeat. To do this, all the rules and moves are already inbuilt into the software.

    Strong AI –

     Strong intelligence that is used to describe a certain mindset of AI development. The goal of this is to develop Artificial intelligence at the point where the intellectual capacity of machines is functionally equal to humans. Strong AI builds machines that can actually think and act like humans. Right now there are no proper examples of this, but some industry has come very close to building a strong AI.

    Second Category:-

    Reactive Machines – 

    This machine is very basic because it does not store memory and cannot even use its past experiences to perform a task in the future. Reactive machines react to it just by looking at it. A good example is IBM’s Deep Blue, which defeated the Grand Master of Chess Kasparov.

    Self-Awareness – 

    This is an artificial intelligence that has its own consciousness, self-awareness, and superintelligence. In simple words, you can also call it a kind of human. But this type of bot is not available yet. If this is possible in the future, it will be a big achievement for AI.

    Limited Memory –

    These are AI systems that can use previous experiences to inform future decisions. Some decision-making functions are designed in a self-driving car.

    Theory of mind –

    This type of AI machine is enabled to communicate, sense, believe, think, expect, and socialize people. Although a lot of experiments have been done in this field, no such thing has come out so that it can be possible.

    Best AI Courses:-

    Let’s discuss Machine Learning now.
    Nowadays, in the growing phase of technology, many such discoveries have been made which are making the impossible possible. So there is also such a discovery machine learning which has played an important role in our lives, people in today’s time do not like writing but prefer to speak.
    So people do any search only by speaking, and not by writing, this possibility of speaking has been created with the help of machine learning, let’s discuss this topic without delay, you can read this post completely and know about machine learning.

    What is Machine Learning

    machine learning image
    machine learning
    Machine learning is a system that can learn to automate systems through self-improvement and without explicitly coding by the programmer. Success comes with the idea that a machine uses the singular form of data to produce accurate results.
    The machine learning system is based on Artificial intelligence (AI) i.e. Artificial intelligence which is sometimes also called machine intelligence.
    Machine learning merges data with statistical tools to predict an output. This output is used by the corporate to create actionable insights. Machine learning is related to data mining and Bayesian predictive modeling. The machine receives the data as input i.e. uses an algorithm to prepare the answer.
    A general machine learning task is to provide a recommendation. For example, for those who have an Amazon Prime account, all recommendations of movies or series are based on the user’s historical data. Technical organizations are using untrained learning to improve user experience with a recommendation for personalization.

    Types of Machine Learning

    It is mainly divided into three parts –
    1.   Supervised learning
    2.   Unsupervised Learning
    3.   Reinforcement Learning

    1. Supervised Learning

    This is the most common part of machine learning in which the output of the program is determined. It works completely on the guidance of the programmer like a teacher teaches a child. First, a model of Algorithm is prepared in it and then a Dataset is created.
    And from this Dataset the machine makes a prediction or takes Decisions. For example, we have made a program in which it is said that Orange is 1$/Kg, Apple is 2$/Kg and Grapes is 2.5$/Kg. So if we ask this machine which fruit price is less than 2$? then the machine will immediately answer that it is “Orange” based on its dataset. Therefore, the output is accurate.

    2.Unsupervised Learning

    In this algorithm of machine learning, Dataset is not fully labeled so that the output is not fully confirmed. This algorithm is used to extract hidden data from large datasets.
    In Unsupervised Learning, the machine itself keeps searching for new patterns and relationships from the data. And it keeps making changes in its dataset. In this, very little information is given to learning the machine and it keeps learning a lot from the same data.

    3. Reinforcement Learning

    These algorithms are quite different and they are being used the most in today’s advanced technology. These are the Self Dependent Algorithm in a way that is capable of taking completely different types of the decision itself. Such programs make many mistakes and keep improving their programs with their mistakes and experience.
    Reinforcement Learning is quite complex, which can also modify the software created when needed. An example of this can be considered as Auto Driving Cars which always goes to the new area and always sees and understands different things.
    Machine learning is also used for various tasks such as
    1. Portfolio optimization
    2. Automated tasks
    3. Search Engine Result Refining
    4. Videos Surveillance
    5. Virtual Personal Assistants
    6. Email Spam and Malware Filtering etc.

    How Machine Learning Works

    Machines are trained to make an accurate prediction, the machine looks at an example. When we give a similar example to the machine. So it can detect the result. However, like a human, if an unseen example of it is given. So the machine has difficulties in predicting.
    The main objective of machine learning is learning and inference. First, the machine learns through pattern discovery. This is thanks to discovery data An important part of data learning is to choose carefully what data to provide to the machine. The list of features used to solve a problem is called a feature vector.
    You can think of a feature vector as a subset of data that is used to deal with a problem. The machine uses some elegant algorithms to simplify reality and transform this discovery into a model. Therefore, the learning curve is used to describe the data and summarize it into a model.

    Best Machine Learning Courses:-

    1. Machine Learning By Coursera Offer By Stanford University
    2. Machine Learning Courses By Udemy
    3. Machine Learning Courses By edX
    4. Introduction to Machine Learning By Udacity(Free Course)
    5. Machine Learning Certification Course by Simplilearn

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