Artificial Intelligence Tutorial | AI Tutorial for Beginners | Artificial Intelligence | Simplilearn
![*](https://i0.wp.com/allprowebdesigns.com/wp-content/uploads/2024/01/1704838302_maxresdefault.jpg?resize=840%2C430&ssl=1)
Video Title: Artificial Intelligence Tutorial | AI Tutorial for Beginners | Artificial Intelligence | Simplilearn
Welcome to artificial intelligence tutorial my name is richard kirschner i’m with the simply learn team that’s www.simplylearn.com get certified and get ahead what’s in it for you today what is artificial intelligence so we’ll start with a general concept types of artificial intelligence covering the four main areas ways of achieving artificial
Intelligence and some general applications of artificial intelligence in today’s world finally we’ll dive into a use case predicting if a person has diabetes or not and we’ll be using tensorflow for that in the python environment what is artificial intelligence and today’s world is probably one of the most exciting
Advancements that we’re in the middle of experiencing as humans so what is artificial intelligence and here we have a robot with nice little clampy hands hey what am i you are what we call artificial intelligence i am your creator reminding him who programmed him and who he’s supposed to take care of
Artificial intelligence is a branch of computer science dedicated to creating intelligent machines that work and react like humans in today’s place where we’re at with artificial intelligence i really want to highlight the fact that they work and react like humans because that is where the development of artificial
Intelligence is and that’s what we’re comparing it to is how it looks like next to a human thanks any tasks you want me to do for you get me a cup of coffee poof here you go he brings him a cup of coffee that’s my kind of robot i
Love my coffee in the morning you’ll see there’s kind of a side theme of coffee today’s tutorial so here we have a robot that’s able to bring him a cup of coffee seems pretty far out there you know that we have a walking robot that’s doing
This but it’s not as far out there as you think we have automatic coffee pots we have devices that can move an object from one point to the other you know anything from amazon delivering packages or whatever you want all these things are in today’s world we’re right on the
Edge of all this that’s what makes this so exciting such an exciting topic and a place of study so let’s take a look a real general look at types of artificial intelligence in the really big picture i mean we drill into it there’s like machine learning and all kinds of
Different aspects but just a generic level of artificial intelligence so hi there discovered four different types come have a look so we have our kind of einstein looking guy or professor at some college so the very first part that we’re really this is uh as reactive machines they’ve been around a long time
This kind of ai are purely reactive and do not hold the ability to form memories or use past experiences to make decisions these machines are designed to do specific jobs remember i talked about the programmable coffee maker that makes coffee in the morning it doesn’t remember yesterday from tomorrow it runs
Its program even going back to our washing machines they have automatic load balancers that have been around for decades so reactive machines we program to do certain tasks and they have no memory but they’re very functional then we have limited memory this is kind of right where we’re at right now this kind
Of ai uses past experience and the present data to make a decision self-driving cars are kind of limited memory ai we bring up self-driving cars because that’s a big thing especially in today’s market they have all these images that they brought in and videos of what’s gone on before and they use
That to teach the automatic car what to do so it’s based on a limited memory limited memory means it’s not evolving new ideas they have to actually when they need to make a change they have the programming built on the memory but then they have to make those changes outside
Of that and then put the new setup in there so it’s continually reprogramming itself theory of mind these ai machines can socialize and understand human emotions machines with such abilities are yet to be developed of course that’s under debate there’s a lot of places that are working on the theory of mind
And having it be able to cognitively understand somebody based on the environment their facial features all kinds of things and evolve with them so their own they have some kind of reflective ability to evolve with that and finally self-awareness this is the future of ai these machines will be super intelligent
Sentient and conscious so they’ll be able to react very much like a human being although they’ll have their own flavor i’m sure achieving artificial intelligence so how in today’s world right now are we going to achieve artificial intelligence well the main arena right now is machine learning machine learning provides artificial
Intelligence with the ability to learn this is achieved by using algorithms to discover patterns and generate insights from the data they are exposed to and here we have a computer as we get more and more to the human realm of art and official intelligence you see this guy
Splits in two and he’s inside his hidden networks and machine learning programs deep learning which is a subcategory of machine learning deep learning provides artificial intelligence the ability to mimic a human brain’s neural network it can make sense of patterns noise and sources of confusion in the data let’s
Try to segregate different kinds of photos using deep learning so first we have our pile of photographs much more organized than my boxes in my closet of photographs the machine goes through the features of every photo to distinguish them this is called feature extraction bingo figures out the different features
In the photos and so based on those different features labels of photos it says these are landscapes these are portraits these are others so it separates them in there if you’ve ever been on google photos a favorite of mine i go through there and i type in dog and
I get all the images of my dog it’s pretty amazing how it’s able to pull those features out so when we’re achieving artificial intelligence and the deep learning let’s take a closer look and see just how deep learning works there are a lot of models out
There this is the most basic model used this is a neural network there are three main layers in a neural network the photos that we want to segregate go into the input layer and so we have a picture of mona lisa it looks like in a couple
Of people when it goes into the first input layer the arrows are drawn to like individual dots each one of those white dots in the yellow layer or in the input layer would be a pixel in the picture maybe a value for that pixel so the picture of the mountains goes in there
And it fills in that whole input layer and then the next picture of the nightscape goes in there the hidden layers are responsible for all the mathematical computations or feature extraction on our inputs so when you see here we have the two uh kind of orangish red layers here and there’s all these
Lines in there those lines are the weights each one of those represents a usually a float number or a decimal number and then it multiplies it by the value in the input layer and you’ll see that as it adds all these up in the hidden layer each one of those dots in
The hidden layer then has a value based on the sum of the weights and then it takes those and puts it into another hidden layer and you might ask well why do we have multiple hidden layers well the hidden layers function as different alternatives to some degree so the more
Hidden layers you have the more complex the data that goes in and what can they can produce coming out the accuracy of the predicted output generally depends on the number of hidden layers we have so there we go the accuracy is based on how many hidden layers we have and again
It has to do with how complex the data is going in the output layer gives us the segregated photos so once it adds up all these weights and you’ll see there’s weights also going into the output layer based on those weights it’ll say yes it’s a portrait no it’s a portrait yes
It’s a landscape no it’s a landscape and that’s how we get our setup in there so now we were able to label our photos let’s take a look and see what that looks like in another domain we have photograph domain now let’s look at it in the airline ticket so let’s predict
The airline ticket prices using machine learning these are the factors based on which we’re going to make the predictions and choosing your factors is so important but for right here we’ll just go ahead and take a look what we got we have airlines we have the origin airport the destination airport the
Departure date here are some historical data of ticket prices to train the machine so we pull in the old data this is what’s happened over the years now that our machine is trained let’s give it new data for which it will predict the prices and if you remember from our
Four different kinds of machines we have machines with memory well this is the memory part it remembers all the old data and then it compares what you put in to produce the new data to predict the new prices the price is 1 000 that’s an expensive flight of course
If that is a us dollars if it’s india i don’t know what the price range is on that hopefully it’s a good deal for wherever you’re going so we predict the prices and this is kind of nice because if you’re looking way ahead it’d be nice
To know if you’re planning a trip for next year how much those tickets are going to cost and they certainly fluctuate a lot so let’s take a look at the applications of artificial intelligence we’re going to dive just a little deeper because we talked about photos we’ve talked about
Airline ticket prices kind of very specific you get one specific number but let’s look at some more things that are probably more in-home more common right now speaking of home this young gentleman is entering his room of course makes a comment like we all do we walk
Into a room this room is dark isn’t it let’s see what happens when i enter it the sensors in my room detect my presence and switch on the lights this is an example of non-memory machines okay you know it senses you it doesn’t have a memory whether you’ve
Gone in or not some of the new models start running a prediction as to whether you’re in the room or not when you show up when you don’t so they turn things on before you come down the stairs in the morning especially if you’re trying to
Save energy you might have one of those fancy thermostats which starts guessing when you get up in the morning so it doesn’t start the heater until it knows you’re going to get up so here he comes in here and this is one of the examples
Of smart machine and that is one of the many applications of artificial intelligence one of the things we’re developing here in our homes okay bye abruptly he leaves leaving his nice steaming cup of coffee there on the table and when he leaves the sensors detect he’s gone and turn off the lights
Let’s watch some tv did someone say tv the sound sensors on the tv detect my voice and turn on the tv sounds kind of strange but with the google dongle and a google home mini you can actually do just that you can ask it to play a movie
Downstairs on your downstairs tv true also with the amazon fire stick and all the different artificial intelligent home appliances that are coming out pretty amazing time to be alive and in this industry here you have one more application of artificial intelligence and you can probably think of dozens of
Others that are right now on the cutting edge of development so let’s jump into my favorite part which is a use case predict if a person has diabetes and in the medical domain i mean if you read any of these they say predictive a person has diabetes but in the medical
Domain we would want to restate this as what is the risk is a person a high risk of diabetes should just be something on their radar to be looking out for and we’ll be helping out with the use case there we are with a cup of coffee again
I forgot to shave as you can see we’ll start with the problem statement the problem statement is to predict if a person has diabetes or not and we might start with this prediction statement but if you were actually using this in a real case again you would say your
Higher risk of diabetes would be the proper way to present that to somebody if you ran their test data through here but that’s very domain specific which is outside of the problem statement as far as the computer programmers involved so we have a look at the features these are
The things that have been recorded and they already have this data from the hospitals one of them would be number of times pregnant obviously if you’re a guy that would be zero glucose concentration so these are people who’ve had their glucose measured blood pressure age age
Is a big factor an easy thing to look at insulin level so if someone’s on insulin they probably have diabetes otherwise they wouldn’t be taking insulin but that would affect everything else and we add that in there if they’re if they’re using insulin let’s start off with the
Code so we’re going to start off by importing parts of our data set and if we’re going to do this let’s jump into one of our favorite tools we’re going to use the anaconda jupiter notebook so let me flip over there and we’ll talk just briefly about that and then we’ll look
Over this code so here we are in the jupiter notebook and this is an inline editor which is just really cool for messing with python on the fly you can do demos in it it’s very visual you can add pieces of code it has cells so you can run each cell
Individually you can delete them but you can tell the cell to be a comment so that instead of running it it just skips over it there’s all kinds of cool things you can do with jupyter notebook we’re not going to go into that we’re actually going to go with the tensorflow and you
Do have to import all the different modules in your python now if you’re in a different python ide that’s fine it’ll work in just about any of your python ides and i’ll point out the one difference in code when we get to that which is the inline for doing our graph
And in this code we’re going to go through we’re going to load our data up we’re going to explore it we’re going to label it and then we’ll use the tensorflow and then we’ll get down to the end where we’re going to take a look
At how good the model is does it work so let’s start with the top part here and let me go ahead and let’s paste that code in there i’ve added a line in at the top i’ve added the folder because i didn’t put it in the same folder as the
Program that’s running you don’t need to worry too much about that but if you have a folder issue or says file doesn’t exist you probably have it in the wrong folder or you need to add the folder path and then we’re going to import pandas and let me just one of the cool
Things since it’s in internet chrome we can just do that control plus and zoom right in there and we have the import pandas as pd so go ahead and draw a line of there you can see that the pandas as pd add a little drawing thing in there
To make it easier to see pandas is a data set it’s a really nice package you can add into your python usually it’s done as pandas as pd that’s very common to see the pd here let’s circle that it’s basically like an excel spreadsheet it adds columns it adds headers it adds
A lot of functionality to look at your data and the first thing we’re going to do with that is we’re going to come down here and we’re going to read and this is a csv it’s a csv file you can open up the file on your computer if you want if
You need access to the file you can either search it and download it from the main thing or type a note in down below on the youtube video and simply learn will help you get that file also and that’s true of any questions you have on this you can add comments down
There and somebody at simplylearn actually keeps an eye on that and we’ll try to answer those questions for you i put in the full path you don’t have to if you have your data files saved in the same folder you’re running your program in and this is just a csv file comma
Separated variables and with the pandas we can read it in there and we’re going to give it put it right in a variable called diabetes and then finally we take diabetes and you’ll see diabetes.head this is a pandas command and it’s really nice because it just displays the data
For us let me go ahead and erase that and so we go up to the run button up here in our jupyter notebook which runs the script in this that’s a cell we’re in and you can see with thediabetes.head since it’s a pandas data frame it prints
Everything out nice and neat for us this is really great and if i go ahead and do a control minus i can get it all to fit on the same screen and this is the first five lines when you do the head of the data just prints out the first five
Lines in programming of course we start with zero so we have zero one two three and four there we go we have yeah here we go we start with zero we have zero one two three and four we have our titles going across the top and of
Course the first column is number of pregnancies glucose concentration for the second one blood pressure measurements of triceps as we keep going you’ll see you can guess what most of these things are group i’m not sure this is probably the test group i didn’t look it up to see specifically what that is
Anytime you start exploring data and for this example we don’t really need to get too much in detail you want to know the domain what does this information mean are we duplicating information going in that’s beyond the scope of this for right now though we’re just going to
Look at this data and we’re going to take apart the different pieces of this data so let’s jump in there and take a look at what the next set of code is so in our next setup or our next step we got to start looking at cleaning the
Data we got to start looking at these different columns and how are we going to bring them in which columns are what i’m going to jump ahead a little bit and also as we look at the columns we’re going to do our import our tensorflow as tf that’s a common import that’s our
Tensorflow model tensorflow was developed by google and then they put it into open source so that it’s free for everybody to use which is always a nice thing so let’s take a look at what these two steps look like in our jupiter notebook so let’s paste this code in
Here and this is something that happens when you’re messing with data is you’ll repeat yourself numerous times we already have diabetes head so i’m going to put the little pound sign which tells python that this is just going to be a comment we’re not going to run it a
Second time in here we have diabetes dot columns and then diabetes columns to norm equals diabetes columns to norm.apply lambda x let’s talk about that but let me first run that and let’s see that comes out with that and then let’s put the next set of code in also
And i’m going to run that let me just hit the run button it doesn’t really have an output but we did a lot of stuff here so let’s go over what we did on this so i’ve turned on my drawing implement to help go through all this
There’s a lot going on here the first thing is we have all of our columns up here all of our column names let me just highlight that and draw a circle around that like we did earlier and we want to look at just specific columns and we’re
Going to start let me change the color on this so it really sticks out i’ll use like a blue i’m going to look at these columns and we’re not going to do age class or group we’re going to look at those in a different way and i’ll
Explain in a little bit what that’s about so we’re looking at number of pregnancies uh glu basically anything that can be read as a float number and you might say age could be read as a float number we’re going to address that differently because when you’re doing statistics we
Usually take the ages and we group them into buckets and we’ll talk about that and so we’re going to take these columns and we’re going to do what they call normalize them call them to normalize what is normalize if you’re using the sk learn module one of their neural
Networks they call it scaling or scalar this is in all of these neural networks what happens is if i have two pieces of data and let’s say one of them is in this case zero to six and one of them is in insulin level is
Zero to maybe point two would be a very high level i don’t know what they actually go to a neural network we’ll look at this six and it’ll go zero to six and as you get into zero six here’s an eight even a higher number it will
Weight those heavier and so it’ll skew the results based more on the number of pregnancies than based on the insulin or based on the blood pressure so we want to level the playing field we want these values to all be in the same area we do
That by normalizing it here and we use a lambda function this is always a kind of a fun thing we take the diabetes it’s a panda set up and let me just erase all that so we can go down here put it back to red and we take our diabetes which is
A panda data set and we only want to look at the columns to normalize because that’s what we built we built a list of those columns and we’re going to apply a lambda function and you’ll see lambda and all kinds of python programming it just means it’s a function we put x in
And whatever is outside of the x over here it then takes that and returns that value so the new value is going to be whatever x is minus x min again it’s a panda’s data set so we can get by with that which is really cool makes it easy to read so we
Take x we look for the minimum value of x and then we divide that by the maximum value x minus the x min or the width and what this essentially does is if you’re going from 0 to 15 it’s going to divide by 15 and then we
Subtract it let’s make this 2 instead of 0 or 2 20 to 115 there we go first off we set this to zero so we subtract x min so right off the bat the minimal value is going to be zero and then we divide it by the difference here x min minus x
Max or x max minus x min this basically sets all the values between zero to one and that’s very important if we want to make sure that it’s not going to be a skew in the results if it’s not going to weight the values one direction or the
Other zero to one that’s what this is doing and again there’s like so many different ways to scale our data this is the most basic scale it’s standard scaling and it’s the most common and we spelled it out in the lambda you can there’s actual um modules that do that
Uh but lambda is just it’s such an easy thing to do and you can see really see what we’re doing here so now we’ve taken these columns we’ve scaled them all and changed them to a zero to one value we now need to let the program know the
Tensorflow model that we’re going to create we need to let it know what these things are so to do that i create a variable called number pregnancy keep it simple and we’ve imported the tensorflow as tf one line of code brings our tensorflow in for us awesome love python
So you get number of pregnancies in tf.feature column.numeric column we’re going to tell it’s a numeric column so it knows that this is a float value zero to one and then number underscore pregnant so we’re taking that column and we’ve reshaped that column from zero to
One and no that doesn’t mean you have a point one pregnancy it just means zero point one equals one or something number pregnant so we’re just mapping this data so tensorflow knows what to do with it and we go through all of these we go through all the way up to again we’re
Not doing age let me just separate h down here these have all been adjusted age we’re going to handle differently we still put it as a numerical column because it is so let me just circle age and group we didn’t even touch group yet we’re going to look at group and h and
We’re going to do some special stuff with that class we’re going to look at last why because this is whether they have diabetes or not that’s what we’re testing for so this is our history we know these people have diabetes or not and we want to find out we want to
Create a model and then we want to test that model predicting whether somebody new has diabetes or not so here i am still with my cup of coffee working away refill number three let’s explore both the categorical group which is going to
Be the b c and d on the end and also the age we’re going to talk about those a little bit more and then we’re going to combine all those features and we’ll jump into splitting the data although that’s kind of the next step but we’ll
Put that all together what are we doing with the age and what are we doing with the group down there so let’s flip on over and take a look at our jupiter notebook so the first thing is let’s put this new set of code in here
And run it and let’s talk about what we’re doing on this set of code in i actually want to go one more step forward on the next set of code or part of it there we go and i can go and just run that while we have it that’s fine
Let’s just scroll up a little bit here and let’s go through what we just did with this set of code if we go back up here to the top part we have group b c b b c we learned from the header so when i come down here we’re going to create a
Signed group and let me get my drawing pad out so we’re looking at this first one up here assigned group tf feature column categorical column with vocabulary list that is a mouthful this is saying that this is a categorical list in this case we have a b c d you
See this a lot in tensorflow a lot of times tensorflow is used to analyze text or logs error logs and they’re just full of all this information and they take these in this case a b c d but in other cases it’d be words and they create
Numbers out of them so it has 0 this is one column this is another column this is another column this is another column and it’s just this way of being able to look at this in a different light and assigning them different values you really don’t want to assign this a float
Value first thought might be let’s assign this 0 to 3 0 1 2 3 for a b c d but if you did that it would try to add 1 plus 2 to get 3 as part of its math and functions and that just isn’t true c
Does not equal a plus b c is just a categorical name it could have been the frank group and laura’s group in a bids group but it’s not you know these are they’re not float numbers so very important you have that distinction in your data the next step we’re looking at
Is our map plot and we’re going to do this this is a cool useful tool we’re going to import matplotlibrary.pi plot as plt that’s standard import and this next line here is really important we’re doing the matplot library we want it to be inline that’s because we’re in
A jupyter notebook remember i told you before that if you’re running this in a different ide you might get a different result this is the line you need if you want to see the graph on this page and then we do diabetes we’re going to look at just
The age and dot hist bins equals 20. what the heck is that well it turns out that because this is remember it’s a pandas there’s our pd pandas as pd because this is a pandas panda automatically knows to look for the matplot library and a hist just means
It’s going to be a histogram and we’re going to separate it into 20 bins and that’s what this graph is here so when i take the diabetes and i do a histogram of it it produces this really nice graph we can see that at 22 most of the participants in this probably around
174 of the people that were recorded were of this age bracket they’re right here in this this first column here and as they get further and further down they have less and less and less until when you’re at 80 you can actually see down here there’s almost
None in a couple of these categories and this is important to know because when you’re doing census you know the older the the group is the more the people have passed away so you’re going to have this in any of your senses as you take you’re always going to have a much
Larger group in the younger crowd and then it gets lower and lower as they get older and i mentioned the word buckets here we go we’re going to create an age bucket we’re going to put people in buckets can you imagine people sitting in buckets kind of a funny way of
Looking at it this just makes it easier for us to separate it when i go to the doctor’s office i don’t want to be told well your age 22 this is you know you’ll get a decent one at 22. here we are back to my cup of coffee i
Told you coffee was an underlying theme these next couple steps are very specific to tensorflow up until now we had some tensorflow as we set the categories up but you’d have to set that up with any model you use to make sure they’re set correctly they’d be set up a
Little differently but we run input functions and in tensorflow this allows for some really cool things to happen and this is why tensorflow is so predominant in a lot of areas so let’s take a look at these next couple lines of code here we’re going to create the
Input function and we’re going to go ahead and create the model and let me go ahead and put these codes in here i went ahead and split it up so it’s different lines so we can talk a little bit about that and then the actual model and let
Me go ahead and run this so we can see what that looks like and in my particular environment it just prints out a little bit of information about the model not too worried about looking at that but we do want to take a closer look at what we did here so we’ve
Created an input function and again this is very specific to tensorflow with the input function we have our train we have our x equals x train and our y equals y train because we want to train it with a particular information but we have these other settings in here these two
Settings and the number of epics is how many times it’s going to go over our training model epic means large that means all the data so we’re going to go over it a thousand times that’s actually a huge overkill for this amount of data usually it only needs probably about 200
But you know when we’re putting it together and you’re trying things out you just kind of throw the numbers in there and then you go back and fine-tune it sometimes the batch size is really important this is where tensorflow does some really cool things if you’re processing this over a huge amount of
Data and you try to batch everything at once you end up with a problem this means we’re only going to read 10 lines at a time through our data so each one of those rows of testing they’ve done we’re only going to look at 10 of them
At a time and put that through our model and train it and then shuffle self-explanatory we’re just moving we’re just randomly selecting which data and what order we go in that way if there’s like you know five in a row that are kind of weighted one way and vice versa
It mixes them up and then finally we create our model so the model goes in there and goes okay i have a tf.estimator.linearclassifier we’re going to put in the feature columns equals feature columns we defined all our columns up above and then we have in classes equal 2 and that
Just means we have our out result is zero or one you have diabetes or you don’t have diabetes or in this case we actually call it high risk of diabetes and then i put one more line of code in there which i forgot we’ve set up our
Model we set up our feature columns now we need to actually train it model.train you’ll see this so common in so many different neural network models this is like a standard what’s different though is we have to feed it the function remember we created this function with all this information
On it and then we have steps in uh steps similar to number of batches and batch size it’s more like a individual lines we step through a thousand is a lot more common for steps than epics but steps is used you probably leave this out in this particular example and let’s go ahead
And run this all together because it has a site when we start training it we get a different output so here we go i’ve run it it’s given me the information from when i created the model and now it just starts going through and we get this information tensor loss equals
Global step all kinds of stuff going on here and if you’re following this it’s just going through the steps and training it it gives you information you could dig deep into here but for this particular setup we’re not going to go too deep on what’s going on just know
That we’ve trained our model this model now has the information we need in it to start running predictions so as we sip our next um take our next step of coffee or maybe it’s tea or if you’re one of those strange late night workers maybe it’s a sip of wine
We go into the next step and we actually want to run some predictions on here but we don’t want to run the training we want to run the test on there we want to take our test data and see what it did and so that’s what we’re going to do
Next is we’re going to run the test through and actually get some answers so if you were actually deploying it you would pull the answers out of the data it’s bringing back let’s take a look at that in our jupyter notebook so here we go let’s paste it in there i’m going to
Go ahead and run it and you’ll see that as it goes it’s actually putting the answers out if you remember correctly well we’ll walk through this here in just a second but it goes through and it runs each line and it gives you a prediction for each line one at a time
And prints them out so let’s take a look and see what that actually looks like let’s start with this first part we’ve created another function this is just like the other function except for x equals x test and there’s no y why is there no y because we don’t know what
The answer is on this yet we don’t want to tell it the answer we wanted to guess what the answer is so we can evaluate that and see how good it did on that 33 percent of the data so this is our x test batch size is 10 again so if we
Were watching this roll down here we would see that it actually processes it 10 lines at a time it’s only going to go through once it goes through all the x test data one time we don’t need to have it predict multiple times and shuffle equals false
Very important we set the shuffle to false because if we were tracking this and actually giving people answers we want to make sure it connects to the right person so they get the right results of course we’re just doing this to evaluate it so let’s take a look down
Here what i put out and as i scroll down to my jupiter notebook we have some information as far as a tensorflow running and then the actual output and if we look at the output we know by this first bracket in python it’s an array we know
By this squiggly bracket that this is a dictionary so that this is a label the dictionary has logistic probabilities class ids classes and so this whole first part let me redo that this whole first part is one output and we know that because there is the bracket there and there is this
Bracket here for the dictionary and it’s a dictionary of terms so if i pulled this out and i looked at object 0 in here i would go down to and let me just find it here it is classes remember how we define classes we define classes as
Our answer and so it’s guessing our tensorflow says based on all the information you gave me i’m guessing this first entry of our first test data is high risk diabetes uh oh go get tested change your diet watch what you’re eating you know high risk one and
If we go down far enough we can see down here is another one where classes equal zero i skipped a couple because they’re all ones up here the b this particular output b in front of the one means it’s a binary output it only comes out as zero or one
And there’s a lot of other information in here you can dig deep into tensorflow and explain these different numbers that’s way beyond the scope of what we’re working on today so the basics of it though is you get an output and we have an output of whether the person has
Diabetes or not well in this case it’s high risk of diabetes or not so now that we’ve run our predictions take a sip of coffee a little short break and we say well what do we need to do well we need to know how good was our predictions we
Need to evaluate our model so if you’re going to publish this to a company or something like that they want to know how good you did let’s take a look at what that looks like in the code so let’s paste that in here and just real quick go back over this by now
This function should look very this is we’re going to call it eval input function it should be pretty straightforward here we have our tf estimator inputs pandas input function and then we have our x test our y test because we this time we want to give it
The answer so it has something to see how good it did on batch is 10 processing 10 at a time we’re only going once through the data we’re not shuffling it although it doesn’t really matter with this and then we’re going to do results equals model.evaluate and we put our evaluation
Function in there this should all look familiar because we’re repeating the same thing it’s very similar with a couple different changes as far as what we’re feeding it and the fact that we’re doing an evaluation let’s see what that looks like when we run it and we go up
Here to our run model you’ll see warnings on some of these because i didn’t install this on its own install so a lot of it is just temporary files because i’m using the jupyter notebook instead of setting it up on a regular machine and we get our output and we’ll
Go ahead and just look at this output real quick and then we’ll flip over and from here you’ll see that it generates an accuracy a baseline average loss the mean gives you a precision prediction it gives you all kinds of information on here so let’s flip over and see what’s
Important and we’ll look at the slide here we are in this slide and this should be exciting because we just about wrapped up our whole tensor flow and tensorflow is one of the more complicated models out there so give yourself a pat on the back for getting
All the way through this when we look at this output we have an accuracy of 0.7165 that’s really what we want to look at that means that we have an accuracy if you’re just truncating it of 71 percent that’s quite good for our model you know
Given a small amount of data we came up with the 71 percent of letting people know they’re high risk or not with diabetes so we created a model that can predict if a person has diabetes based on some previous records of people who were diagnosed with diabetes and we’ve
Managed to have an accuracy of 71 percent which is quite good the model was implemented on python using tensorflow again pat yourself on the back because tensorflow is one of the more complicated scripts out there it’s also one of the more diverse and useful ones so the key takeaways today is we’ve
Covered what is artificial intelligence with our robot that brings us coffee and we noted that we are comparing it to how it reacts and looks like humans very important to note that in today’s world where we’re at and we looked at types of artificial intelligence from the reactive machines to limited memory and
Looking into the future of theory of mind and self awareness then we went in there and took a look at taking photos and how artificial machine learning work we took a glance at deep learning with our neural networks and how we have hidden layers in our input layer and our
Output layer we then looked at a use case of somebody walking into a room and activating their tv using artificial intelligence or part of the ai category and finally we dug in deep and we did some coding in the tensorflow in python with that let’s wrap it up i’d like to
Thank you for joining us today for more information visit www.simplylearn.com get certified get ahead again if you have any questions or would like some verifications or or more information please feel free to add a note in the youtube video comment section we look forward to hearing from you and i wish
You a wonderful day hi there if you like this video subscribe to the simply learn youtube channel and click here to watch similar videos turn it up and get certified click here
-
Sale!
Wireless WIFI Repeater Extender Amplifier Booster 300Mbps
$29.99$14.99 Add to cartWireless WIFI Repeater Extender Amplifier Booster 300Mbps
Categories: Electronics, Wi-Fi Router, Wireless Wi-Fi Extender Tags: 300Mbps, 802.11N, Amplifier, Booster, Extender, mobile wi-fi booster, Remote, WIFI, Wireless, Wireless WIFI, Wireless WIFI Repeater, Wireless WIFI Repeater Extender, Wireless WIFI Repeater Extender Amplifier, Wireless WIFI Repeater Extender Amplifier Booster, Wireless WIFI Repeater Extender Amplifier Booster 300Mbps$29.99$14.99 -
Sale!
Full RGB Light Design Gaming Headset Headphones with Mic
$24.99$14.99 Add to cartFull RGB Light Design Gaming Headset Headphones with Mic
Categories: Electronics, Gaming, Gaming Headsets Tags: Design, Full, Full RGB Light Design Gaming Headset, Full RGB Light Design Gaming Headset Headphones, Full RGB Light Design Gaming Headset Headphones with Mic, Gamer, Gaming, Gaming Headset Headphones, gaming headset wireless, Headphone, Headphones, Headset, Light, Mic, Package, RGB$24.99$14.99 -
Sale!
Wireless BlueTooth Multi-Device Keyboard Mouse Combo
$39.99$19.99 Add to cartWireless BlueTooth Multi-Device Keyboard Mouse Combo
Categories: Electronics, Gaming, Gaming Keyboards, Keyboard Mouse Combos Tags: Combo, Keyboard, keyboard mouse combos, Mouse, MultiDevice, Set, WireKeyboard Mouse Combo, Wireless, Wireless BlueTooth Keyboard Mouse Combo, Wireless BlueTooth Keyboard Mouse Combos, Wireless BlueTooth Multi-Device Keyboard Mouse Combo, Wireless BlueTooth Multi-Device Keyboard Mouse Combos$39.99$19.99 -
Sale!
High Back Leather Executive Adjustable Swivel Gaming Chair with Headrest and Lumbar
$199.99$139.99 Add to cartHigh Back Leather Executive Adjustable Swivel Gaming Chair with Headrest and Lumbar
Categories: Gaming, Gaming Chairs Tags: Adjustable, Chair, computer chairs, Desk, Executive, Gaming, Girl, Headrest, High, High Back Leather Executive Adjustable Swivel Gaming Chair, High Back Leather Executive Adjustable Swivel Gaming Chair with Headrest, High Back Leather Executive Adjustable Swivel Gaming Chair with Headrest and Lumbar, High Back Leather Executive Adjustable Swivel Gaming Chairs, Leather, Lumbar, Office, Racing, Swivel$199.99$139.99 -
Sale!
Professional LED Light Wired Gaming Headphones with Noise Cancelling Microphone
$29.99$19.99 Select optionsProfessional LED Light Wired Gaming Headphones with Noise Cancelling Microphone
SKU: N/A Categories: Electronics, Gaming, Gaming Headsets Tags: Cancelling, Gaming, Gaming Headphones with Noise Cancelling Microphone, gaming headset, Headphones, Headset, LED, Light, Mic, Microphone, Noise, Professional, Professional LED Light Wired Gaming Headphones, Professional LED Light Wired Gaming Headphones with Noise Cancelling Microphone, Wired, Wired Gaming Headphones, Wired Gaming Headphones with Noise Cancelling Microphone$29.99$19.99 -
Sale!
Gaming Desk with LED Lights USB Power Outlets and Charging Ports
$349.99$249.99 Select optionsGaming Desk with LED Lights USB Power Outlets and Charging Ports
SKU: N/A Categories: Computer Desk, Gaming, Gaming Desk Tags: and Charging Ports, Charging, Desk, Desks, Gaming, gaming desk with led lights, Gaming Desks with LED Lights, Home, LED, Lights, Monitor, Office, Outlets, Port, Power, Room, Stand, USB, USB Power Outlets, White, Workstation$349.99$249.99 -
Sale!
Wired Mixed Backlit Anti-Ghosting Gaming Keyboard
$99.99$79.99 Add to cartWired Mixed Backlit Anti-Ghosting Gaming Keyboard
Categories: Electronics, Gaming, Gaming Keyboards Tags: Antighosting, Backlit, Blue, brown, Gaming, Gaming Keyboard, gaming keyboards, gaming keyboards and mouse, Keyboard, Laptop, Switch, Wired, Wired Mixed Backlit Anti-Ghosting Gaming Keyboard, Wired Mixed Backlit Anti-Ghosting Gaming Keyboards, Wired Mixed Backlit Gaming Keyboard$99.99$79.99 -
Sale!
Wireless Bluetooth 5.3 ANC Noise Cancellation Hi-Res Over the Ear Headphones Headset
$119.99$59.99 Add to cartWireless Bluetooth 5.3 ANC Noise Cancellation Hi-Res Over the Ear Headphones Headset
Categories: Electronics, Gaming, Gaming Headsets Tags: 5.3 ANC Noise Cancellation Hi-Res Over the Ear Headphones Headset, ANC, Audio, Bluetooth, Cancellation, Ear, Earphone, gaming headset, Headphones, Headset, Hi-Res Over the Ear Headphones Headset, HiRes, Noise, Wireless, Wireless Bluetooth 5.3 ANC Noise Cancellation Hi-Res Headphones, Wireless Bluetooth 5.3 ANC Noise Cancellation Hi-Res Over the Ear Headphones Headset, Wireless Bluetooth 5.3 ANC Noise Cancellation Hi-Res Over the Ear Headphones Headsets$119.99$59.99 -
Sale!
Wired Sports Gaming Headset Earbuds with Microphone
$19.99$9.99 Select optionsWired Sports Gaming Headset Earbuds with Microphone
SKU: N/A Categories: Gaming, Gaming Headsets Tags: Accessories, Earbud, Earphone, Earphones, Gaming, gaming headset with microphone, Headphones, Headset, IOS, Microphone, Sports, Wired, Wired Sports Gaming Headset Earbuds, Wired Sports Gaming Headset Earbuds with Microphone, Wired Sports Headset Earbuds$19.99$9.99 -
Sale!
150W Universal Multi USB Fast Charger 16 Port MAX Charging Station
$49.99$29.99 Add to cart150W Universal Multi USB Fast Charger 16 Port MAX Charging Station
Categories: Charging Stations, Electronics Tags: 150W, 150W Charging Station, 150W Universal Multi USB Charging Station, 150W Universal Multi USB Fast Charger 16 Port MAX Charging Station, 150W Universal Multi USB Fast Charger 16 Port MAX Charging Stations, 150W Universal Multi USB MAX Charging Station, 16 Port MAX Charging Station, 3.5A, Charger, Charging, Fast, laptop charging stations, Max, Multi, Port, Stand, Station, Universal, USB$49.99$29.99
🔥Become An AI & ML Expert Today: https://taplink.cc/simplilearn_c_ai_ml
I am lost in the middle of the video…
You've excelled! To augment this, a similar book would be a terrific companion. "From Bytes to Consciousness: A Comprehensive Guide to Artificial Intelligence" by Stuart Mills
Thank You , This Video will be my First step for AI .
AI is going to take over the world 🌏🌋🏙🏚
Great Information for the beginners .Its such an interesting time we are living in .So exciting .Thank you so much for the video 🙂
Is this like Robo Cop?
I would like to know the difference in AI program output with a CPU and the output using GPU.
So clear explanation, thanx so much
Let's say I want to create forex robot
Is there anyone with free help to me
this is very nice
EXCELLENT VIDEO!!!
Could you please send me the CSV file? Thanks
Very informative , whole lotta thanks for the post tbh
I love it bro
🔥🔥🔥
A robot wouldn’t ask what is it. It hasn’t got the capacity of thought
Good tutorial
Need to install these packages in the environment beforehand to run the code
1. pandas
2. tensorflow
3. matplotlib
4. scikit-learn
If "tf.estimator.inputs.pandas_input_fn" doesn't work, change to "tf.compat.v1.estimator.inputs.pandas_input_fn"
Good stuff
i created an ai and became my school head boy at just the age of 12
letss go
Hey could i please get the files? My email is jclauson32@gmail.com– thanks!
I want an AI friend