What is Machine Learning

Hello there my name is Gary Sims from Andrew authority now one area of computing .

That is improving the way we use our smartphones and use the web is machine learning now sometimes machine learning in AI get used interchangeably especially by big brand companies that want to announce their latest innovations however machine learning and AI are quite two distinct areas of computing.

Her of course they are connected and today we’re going to ask ourselves the question Hhat is machine learning the goal of AI is to create a machine that can mimic a human mind and do.

That of course it needs learning capabilities however it’s more than just about learning it’s also that knowledge representation reasoning and even things like abstract thinking machine learning on the other hand is solely focused on writing software that can learn from past experience one thing you might find quite astounding is that in fact.

Machine Learning and Data mining

machine learning is more closely related to data mining and statistics than it is to AI well why is that well first of all we need to look at what we mean by machine learning one of the standard definitions of machine learning as given by Tom Mitchell a professor at Carnegie Mellon University.

Is this a computer program is said to learn from experience II with respect to some class of tasks T and performance measure P if it’s performance at tasks in T as measured by P improves with experience II okay we’ll let me try to put that more simply for you.

If a computer program can improve how it performs a certain task based on past experience then you can say it has learned this is quite different to a program which can perform a task because its programmers have already defined all the parameters and data needed to perform that task.  fFr example a computer program can play tic-tac-toe maybe you call it noughts and crosses because a programmer wrote the code with a built-in winning strategy however a program that has no predefined strategy and only a set of rules about the legal moves will need to learn by repeatedly playing the game until it is able to win this doesn’t only apply to games is also true of programs which perform classification and prediction classification.

Is the process whereby a machine can recognize and categorize things from a data set including from visual data and measurement data prediction known as regression in statistics is where a machine can guess predict the value of something based on previous values for example given a set of characteristics about a house how much is it worth based on previous house sales and this leads us to another definition of machine learning.

It is the extraction of knowledge from data you have a question you are trying to answer and you think the answer is in the data that is why machine learning is related to statistical analysis and data mining machine learning can be split into three categories supervised learning unsupervised learning and reinforcement learning let’s have a look at what they mean supervised learning .

Is where you teach train the machine using data which is well labeled that means that the data is already tagged with the correct answer to correct outcome here is a picture of the letter A this is a flag for the United Kingdom it has three colors one of them is red and so on the greater the data set the more the machine can learn about the subject matter

After the machine is trained its given new previously unseen data and the learning algorithm then uses the past experience to give you an outcome this is the letter A that is the UK flag and so on unsupervised learning is where the machine is trained using a data set that doesn’t have any labels the learning algorithm is never told what the data represents here is a letter but no other information about which letter it is here are the characteristics of a particular flag without naming.

Model about how language works

That flag unsupervised learning like listening to a podcast in a foreign language which you don’t understand you don’t have a dictionary and you don’t have a supervisor or teacher to tell you what you are listening to if you listen to just one podcast it won’t be much benefit to you but if you listen to hundreds of hours of those podcasts your brain will start to form.

A model about how language works you will start to recognize patterns and you’ll start to expect certain sounds when you do get hold of dictionary or a tutor then you will learn that language much quicker reinforcement learning is similar to unsupervised learning in that the training data is unlabeled however when asked a question about the data the outcome will be graded a good example of this.

Is playing games if the machine wins a game then the result is trickled back down through the set of moves to reinforce the validity of those moves this isn’t much due to the computer play just one or two games but if it pays thousands even millions of games .

Then the cumulative effect of the reinforcement will create a winning strategy there are many different techniques for building machine learning systems and many of these techniques are related to data mining and to statistics.

for example if I have a data set which describes different types of coins based on their weight and based on their diameter I am able to use a technique known as nearest neighbor to help classify previously unseen coins with nearest neighbor the new coin is compared to the nearest neighbors around it and see what classification.

They have it’s then given the same classification. as its nearest neighbors now you can pick how many neighbors you want to compare against and that number is often referred to as K so therefore the full title for this algorithm is K nearest neighbors however there are lots of other algorithms that try to do the same thing but using different methods.

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