1. WELCOME TO AI PROJECT
CYCLE
STUDENTNAME=SHIDHANT MITTAL
CLASS=9D ROLLNO.38
SUBJECT=COMPUTER
TOPIC=AI PROJECTCYCLE
SCHOOLNAME=CREANE MEMORIAL HIGH
SCHOOLGAYA
3. ï PROJECT CYCLE
ï PROJECT CYCLE ARE THE STEPS INVOVED IN CREATING A PROJECT,
STARTING FROM THE GIVEN PROBLEM TILL THE PROJECT IS CREATED
AND TESTED.
ï AI IS A FORM OF INTELLIGENCE,ALONG WITH TYPE OF TECHNOLGY AND
ONE OF THE LATEST FIELDS OF STUDY. WITH THE DEVELOPMENT IN THE
FIELD AI(BITH AMCHINES ADND SOFTWARE). AI CORE,S IDEA IS
BUILDING SMART MACHINES AND ALOGRITHMS WHICH ARE CAPABLE OF
PERFORMING COMPUTATIONAL TASKS THAT WOULD REQUIRE
HUMANITIES BRAIN FUNCTIONS.
ï AI IS DEVELOPED AND VARIOUS STEPS ARE INVOLVED IN SUCH
PROJECT:
4. âŠCOMPONENTSOF AI PROJECTCYCLE:
⊠PROBLEM SCOPING:UNDERSTANDING THE
PRPBLEM
⊠DATA ACQUISITION:COLLECT ACCURATE AND
RELIABLE DATA
⊠DATA EXPLORATION:ARRANGING THE DATA
UNIFORMLY
⊠MODELLING:CREATING MODELS FROM THE
DATA
⊠EVALUATION:EVALUATING THE PROJECT
5. ï PROBLEM SCOPING
ï IT REFERS TO THE UNDERSTANDING A PROBLEM
AND IDENTIFYING OUT VARIOUS FACTORS WHICH
AFFECT THE PROBLEM;DEFINE THE GOOD ORIGIN OR
AIM OF THE PROJECT.
ï 4 W,S OF PROBLEM SCOPING:
ï 1)WHO 2)WHAT 3)WHERE 4)WHY
6. HOW TO IDENTIFY A PROBLEM
SCOPING IN AI:
ï WHO:
ï THIS PART HELPS US IN
COMPREHENDING AND
CATEGORIZING WHO ALL
ARE AFFECTED DIRECTLY
AND INDIRECTLY WITH THE
PROBLEM AND WHO ARE
CALLED STAKE HOLDERS.
ï
ï WHERE:
ï IT SHOWS THAT FROM
WHERE PROBLEM
ARISES,SITUATIONS AND
THE LOCATION.
ï WHAT:
ï THIS PART HELPS US IN
UNDERSTANDING AND
IDENTIFYING THE NATURE
OF THE PROBLEM AND
UNDER THIS BLOCK,WE ALSO
GATHER EVIDENCE TO PROVE
THAT THE PROBLEM WE
HAVE.
ï WHY:
ï âWHY IS THE PROBLEM
WORTH SOLVINGâ
7. Problem statement Template:-It helps
to summarize all the key points into one single. Template so
that in future, whenever There is need to look back at the
basis of the problem, we can take a look at the template and
understand the key elements of it.
ï Follow the following steps to identify
the problem Scoping from the
project:
ï Understand why the project was started.
ï Define the project's primary objectives .
ï Outline the project work statement.
ï Determine the most important goals.
ï Make a list of scope exclusions .
ï Choose important milestones.
8. ï Data Acquisition:- The method of collecting connect
and dependable data to work with is known as data
acquisition Data can the in the form of text, video, photos,
audio and it can be gathered from a variety of places such
as websites, journals and newspapers.
ï These us two types of data-
ï 1. Structured data - When data is in standardized form
has a well-defined structure, follows a consistent order, and
is easily accessible by humans and programs. The data is
in the far in of no characters etc.
ï 2- Unstructured data- II is a information that dont follow
traditional data models and is therefore difficult to store
and manage videos, audio and image files.
9. ï Dataset is a collection of data un tabular formed.
It contains numbers or variable that are related
to Specific subject.
ï Significance of big data:
ï Massive amount of data is used, called as big
data.
1) Big data helps un machine learning.
2) It is useful to discover patterns
3) It is the techniques may ideality the likelihood
of future occasions.
4) It can be used for solving complex problems
and achieving better goals.
5) Is the process of growth.
10. Big data does have big challenges:
1) Storage
2) Security 3) Curation
ï There are three important parameters of
big data:
ï Volume - It is important that we have a lot
of data in order to build an accurate AI
system.
ï velocity - This means the speed at which
the data is taken.
ï variety - A large volume unstructured data
would eventually form set patterns, and A I
system us built in a way that is able to
identify and train itself to make sense
of data for long runs.
11.
12. Datavisualization: It is the graphical represent -ation of data
that we symbols to convey a story and help people
understand large volumes of information.
ï Data Exploration- In order to better
understand the nature of the data, data
analysts utilize data visualization and
statistical God's to convey dataset
characterizations, such as sign, amount and
accuracy.
ï Significance - I helps us to gain a better
understanding a dataset, making it easier to
explore and use it later. It also helps to
quickly understand the data's trends and
patterns Data integration.
13. âąThe following are the some of the most
frequent data visualisation:
a. Column chart - It uses vertical columns to
represent a lot a series. Because column lengths.
are easy to complete demonstrate the changes
effective approach in the data.
14. ï Bar chart- This data is displayed in a bar multiple bars,
each representing different category.
ï Fusion charts: It can produced 90 different chart types
and integrates with a large no. of platforms and
frameworks giving a great deal of flexibility .
ï High charts: It allows for deep customization, & styling
be done. It is also extend -able and pluggable for experts
seeking advanced animations and functionally.
ï Tableau- 9t is often regarded on the grand master of data
visualization Software for good reason. It is widely used for
business intelligence.
15. ï Artificial Intelligence (AI): It refers to any technique that
enables computers to mimic human intelligence. The Al-enabled
machines think algorithmically and execute what they have been
asked for intelligently
ï Machine Learning (ML): It enables machines to improve at
tasks with experience. The machine learns from its mistakes and
takes them into consideration in the next execution. It improvises
itself using its own experiences.
ï Deep Learning (DL): It enables software to train itself to
perform tasks with vast amount of data. In it the machine is
trained with huge amount of data which helps it into training itself
around the data. Such machine is intelligent enough to develop
algorithms for itself.
ï VENNDIAGRAMOF AI:
16.
17. ïModelling:
ï An AI us a program that has been drained
to recognize patterns using a not dok a.
AI modeling is the process of creating
algorithms, also known as models, that
may be educated to produce intelligent
results. This is the process of
programming code to create a
machine artificially.
18. Decision Tree un AI:-Thisconcept us similarto that of Storyof Speakers It is a rule-
basedAI modelthat usesnumerousjudgments to assist the machine in determining
what an element us.
The following structure of a decision tree':
Root and branches.
ï In AI modelling in which the developer hasn't specified the relationship or
patterns in the data. Random data us provided to the computer in this method
and the system is left to figure out patterns and trends from it. When the date us
unable and too random for a human to make some of this method is usually
used.
19. ï Points to Remember while creating
decision tree:
ï When creating Decision Trees, one, should
carefully. examine the dataset provided and day
to do what pattern the output lead follows. Try
picking one output and figuring out the common
links. Hat all similar outputs have based on it
when building a decision tree, its common for
the dataset lo have redundant material that's of
no me. As a result, we should make a list of the
parameters that directly affect the output and
we only those when designing a single tree.
20. Model deploymentis theprocessof puttingmachine learningmodelsintoproduction.This makes the model's
predictions available to users, developers or systems, so they can make business decisions based on data, interact with their
application (like recognize a face in an image) and so on.
ï EVALUATION:
ï It is the method of understanding the
reliability of an API evaluation and is based
on the outputs which is recieve by the
feeding the data a into the model and
comparing output with the actual answers.
ï 1) Using the system in a controlled
environment.
ï 2) Identifying areas that can be improved.
ï (3) Identifying areas of improvement that car
became areas of future research.
21. ï Example based on this on AI project cycle:
ï Problem Scoping: Lack of ability un linguistic used of
english course due to lack of conversation
ï 2. Data acquisition-âąFinding the common mistakes made
by people during english conversation and making the list
of right or wrong way.
ï 3 ·Data exploration -Finding the common patterns of
error in people and arranging them list of frequency.
ï 4 Modelling -âąCreaking a language processing chatbot
which asks question to assist people and correct them.
ï 5. Evaluation: Releasing the chatbot and seeing out how
it helps people by saying and improvements if required
