Overview of Machine Learning & It s Algorithm

Overview of Machine Learning & It s Algorithm

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  Topic To BeCovered: Overview of Machine Learning & It’s Algorithm Jagdamba Education Society's SND College of Engineering & Research Centre Department of Computer Engineering SUBJECT:Artificial Intelligence & Robotics Lecture No-03 Prof.Dhakane Vikas N
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  Overview of machinelearning  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 learn for themselves.
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  Overview of machinelearning without being explicitly programmed  Explicitly programmed(Hard Coding): Writing out the instructions in order to accomplish a change manually is explicit programming. It is just specifically writing each and every instruction for the machine’s action.  Without Explicitly programmed(No Hard Coding): No need to Write specifically each and every instruction for the machines action.  See the first Line below, the variable firstName will always be hello world. That is called has coded value i.e. Explicit programming.  Second line Not using Hard coded value i.e.”Without being explicitly programmed part”. String firstName=“HelloWorld”; Console.WriteLine(“first name:”); String firstName=Console.ReadLine();
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  SUPERVISED LEARNING APPROACH Suppose you have a basket and it is fulled with different kinds of fruits. Your task is to arrange them as groups. For understanding let me clear the names of the fruits in our basket( Apple, Banana, Grape, Cherry)
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  SUPERVISED LEARNING APPROACH SupervisedLearning You already learn from your previous work about the physical characters of fruits So arranging the same type of fruits at one place is easy now. Your previous work is called as training data in data mining You already learn the things from your train data, this is because of response variable Response variable means just a decision variable You can observe response variable below (FRUIT NAME)
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  Supervised Learning Suppose youhave taken a new fruit from the basket then you will see the size, colour and shape of that particular fruit. If size is Big, colour is Red, shape is rounded shape with a depression at the top, you will conform the fruit name as apple and you will put in apple group. Likewise for other fruits also. Job of grouping fruits was done and happy ending. SUPERVISED LEARNING APPROACH
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  SUPERVISED LEARNING APPROACH SupervisedLearning You can observe in the table that a column was labelled as “FRUIT NAME“. This is called as response variable. If you learn the thing before from training data and then applying that knowledge to the test data (for new fruit), This type of learning is called as Supervised Learning. Classification comes under supervised learning.
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  Un-SUPERVISED LEARNING APPROACH UNSUPERVISELEARNING Suppose you have a basket and it is fulled with some different types fruits, your task is to arrange them as groups. This time you don’t know anything about the fruits, honestly saying this is the first time you have seen them. You have no clue about those. So, how will you arrange them? What will you do first??? You will take a fruit and you will arrange them by considering physical character of that particular fruit.
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  Un-SUPERVISED LEARNING APPROACH UNSUPERVISELEARNING Suppose you have considered colour. Then you will arrange them on considering base condition as colour. Then the groups will be something like this. RED COLOR GROUP: apples & cherry fruits. GREEN COLOR GROUP: bananas & grapes.
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  Un-SUPERVISED LEARNING APPROACH Sonow you will take another physical character such as size. RED COLOR AND BIG SIZE: apple. RED COLOR AND SMALL SIZE: cherry GREEN COLOR AND BIG SIZE: bananas. GREEN COLOR AND SMALL SIZE: grapes Job done happy ending. UNSUPERVISE LEARNING
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  Un-SUPERVISED LEARNING APPROACH UNSUPERVISELEARNING Here you did not learn anything before, means no train data and no response variable. This type of learning is known as unsupervised learning. Clustering-comes under unsupervised learning