Artificial Intelligence Course | AI Tutorial For Beginners | Artificial Intelligence Training | Simplilearn

What’s in it for you?
Ways of achieving artificial intelligence
Types of Artificial intelligence
Applications ofArtificial Intelligence
Use case - predicting if a person has diabetes or not
What is Artificial Intelligence?
content

Brief History of Artificial Intelligence
The word ‘Artificial
Intelligence’ coined by John
McCarthy
‘Shakey’ was the first general
purpose mobile robot built
Supercomputer ‘Deep blue’ was
designed which defeated the
world Chess champion in a game
First commercially successful
robotic vacuum cleaner
created
Speech recognition, RPA,
dancing robots, smart homes
and many more to come from
AI
1956 1969 1997 2002 2005-2018

Hey! What am I?

You are what we call
‘Artificial Intelligence’

I am your creator

Artificial Intelligence is a branch of Computer Science
dedicated to creating intelligent machines that work
and react like humans.

Thanks! Any task you want
me to do for you?

Get me a cup of coffee?

Here you go!

McCarthy
created
AI
1956 1969 1997 2002 2005-2018
Types of Artificial Intelligence

Hi there! I have discovered four
different types of Ai. Come have a
look!

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
Reactive machines

Reactive machines
Limited memory
This kind of AI uses past
experience and the present data to
make a decision. Self driving cars
are a kind of limited memory AI

Theory of mind
These ai machines can
socialize and understand
human emotions. Machines
with such abilities are yet to
be built
Reactive machines
Limited memory

Self awareness
this is the future ofAi.These
machines will be super
intelligent, sentient and
conscious
Theory of mind
Reactive machines
Limited memory
These ai machines can
socialize and understand
human emotions. Machines
with such abilities are yet to
be built

McCarthy
created
AI
1956 1969 1997 2002 2005-2018
Achieving Artificial Intelligence

Machine learning
Machine Learning provides Artificial Intelligence
with the ability to ‘Learn’.This is achieved by
using algorithms that discover patterns and
generate insights from the data they are exposed
to

Deep 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
Machine learning
Machine Learning provides Artificial Intelligence
with the ability to ‘Learn’.This is achieved by
using algorithms that discover patterns and
generate insights from the data they are exposed
to

Achieving Artificial Intelligence – Deep Learning
let’s try to segregate different
kinds of photos using Deep
Learning

Photographs
We provide a large set of
photographs for the machine
to segregate

Photographs
The machine goes through features
of every photo to distinguish them

Photographs
This is called ‘feature
extraction’
Bingo!

Labeled
photographs
landscapes portraits others
Segregated photos
Based on the features of each
photo, it segregates them
Bingo!

Let’s see how deep
learning works!

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
Input layer

The hidden layers are responsible for all
the mathematical computations or
feature extraction on our inputs
Input layer
Hidden layers

The accuracy of the predicted output
generally depends on the number of
hidden layers we have
Input layer
Hidden layers

The output layer gives us the
segregated photos
Input layer
Hidden layers
Output layer
portrait
Landscape

Let’s predict the airline
ticket prices using machine
learning
Achieving Artificial Intelligence – Machine Learning

These are the factors based on
which we are going to make the
predictions

Origin
airport
Destination airportDeparture date
airlines

Here are some historical data of
ticket prices to train the machine
Old data

Now that our machine is trained,
let’s give it new data for which it will
predict the prices
Old data
New data

Old data
New data
The price is $1000!!

McCarthy
created
AI
1956 1969 1997 2002 2005-2018
Applications of Artificial Intelligence

Let’s see what happens
when I enter it!

The sensors in my room
detect my presence and
switch on the lights

That is one of the many
applications of Artificial
intelligence

That’s one of the many
applications of artificial
intelligence
Ok bye!

The sound sensors
on the tv detect my
voice and turn on the
tv!

Here, you have one
more application of
artificial intelligence

McCarthy
created
AI
1956 1969 1997 2002 2005-2018
Use Case – Predict if a person has Diabetes

Use Case
Hi! I’ll be helping you out with
the use case

Use Case
The problem statement is to predict if a
person has diabetes or not!
Predict if a patient has diabetes based on previous
test data
Problem Statement

Use Case
The features are…!
Number of times
pregnant

Use Case
Number of times
pregnant
Glucose concentration

Use Case
Number of times
pregnant
Blood pressure

Use Case
Number of times
pregnant
Blood pressure
age

Use Case
Number of times
pregnant
Blood pressure
age
insulin

Use Case
Let’s start off with the code!

Use Case
#loading dataset
import pandas as pd
diabetes = pd.read_csv('pima-indians-diabetes.csv’)
diabetes.head()

Use Case
#Cleaning Data
cols_to_norm = ['Number_pregnant', 'Glucose_concentration',
'Blood_pressure', 'Triceps','Insulin', 'BMI', 'Pedigree’]
diabetes[cols_to_norm] =
diabetes[cols_to_norm].apply(lambda x: (x - x.min()) /
(x.max() - x.min()))
diabetes.head()

Use Case
#importing tensorflow
Import tensorflow as tf
diabetes.columns

Use Case
#categorical features
assigned_group =
tf.feature_column.categorical_column_with_vocabulary_list
('Group',['A','B','C','D'])
#converting continuous to categorical
import matplotlib.pyplot as plt
%matplotlib inline
diabetes['Age'].hist(bins=20)

Use Case
age_buckets = tf.feature_column.bucketized_column(age,
boundaries=[20,30,40,50,60,70,80])
#combining all the features
feat_cols = [num_preg ,plasma_gluc,dias_press ,tricep
,insulin,bmi,diabetes_pedigree ,assigned_group, age_buckets]
#splitting the data
x_data = diabetes.drop('Class',axis=1)
labels = diabetes['Class’]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test =
train_test_split(x_data,labels,test_size=0.33,
random_state=101)

Use Case
#applying input function
input_func =
tf.estimator.inputs.pandas_input_fn(x=X_train,y=y_train,ba
tch_size=10,num_epochs=1000,shuffle=True)
#creating the model
model =
tf.estimator.LinearClassifier(feature_columns=feat_cols,n_c
lasses=2)
model.train(input_fn=input_func,steps=1000)

Use Case
#prediction
pred_input_func = tf.estimator.inputs.pandas_input_fn(
x=X_test,
batch_size=10,
num_epochs=1,
shuffle=False)
predictions = model.predict(pred_input_func)
list(predictions)

Use Case
#evaluating the model
eval_input_func = tf.estimator.inputs.pandas_input_fn(
x=X_test,
y=y_test,
batch_size=10,
num_epochs=1,
shuffle=False)
results = model.evaluate(eval_input_func)
results

Use Case
So, we have managed to have
an accuracy of 71% and that’s
quite good for our model!

Use Case - Conclusion
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

Use Case - Conclusion
The model was implemented on python
using tensorflow

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