Project Cycle Glossary Class 10

Project Cycle
Problem Scoping
Data Acquisition
Data Exploration
Modelling
Evaluation
4Ws of Problem Scoping
Statement Template
Project Stakeholder
Data Sources
Visualization Tools
Learning Based Approach
Rule Based Approach
Training Set
Testing Set


Project Cycle Overview

Ai Project cycle

What is Project Cycle ?

In earlier classes we have studied about Water Cycle, etc. In water Cycle we studied that how is the journey of water carried out from one step to other till the end.

Like that in Project Cycle we are going to deal with the steps involved in creating a project, starting from the given problem till the project is created and tested.

Project Cycle is a step by step process to solve the problems using proven scientific methods and drawing the inference about it.

Let us take some daily examples as project, requiring steps to solve the problem.

Making Tea


  1. Boil Water .
  2. Put Tea Powder
  3. Put Milk
This was a simple one which was only consisting exploring the items and modelling it into tea. Lets see one more example:

Creating a birthday card.


  1. Checking the factors like budget,etc Which will help us decide the next steps and understanding the Project.
  2. Acquiring data from different sources like online, with friends etc for Designs and ideas.
  3. Making a list of the gathered data.
  4. Creating or Modelling a card on the basis of the data collected.
  5. Showing it to Parents or cousins to Let them check it or evaluate it.

Components of Project Cycle

Components of project cycle are the steps which contributes in completing the Project.

Components of the AI Project Cycle :


Problem Scoping Understanding the problem
Data Acquisition Collecting accurate and reliable data
Data Exploration Arranging the data uniformly
Modelling Creating Models from the data
Evaluation Evaluating the project

Problem Scoping



Problem Scoping Project Cycle

Problem Scoping refers to understanding a problem finding out various factors which affect the problem, define the goal or aim of the project.

4Ws Of Problem Scoping

The 4W's of Problem Scoping are Who, What, Where and Why. These Ws helps in identifying and understanding the problem in a better and efficient manner.

1. Who - "Who" part helps us in comprehending and categorizing who all are affected directly and indirectly with the problem and who are called the Stake Holders

2. What - "What" part helps us in understanding and identifying the nature of the problem and under this block, you also gather evidence to prove that the problem you have selected actually exists.

3. Where - "Where" does the problem arises, situation and the location.

4. Why - "Why" is the given problem worth solving.

Problem Statement Template

The Problem Statement Template helps us 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 Problem Statement Template and understand the key elements of it.

Data Acquisition

Data Acquisition is he process of collecting accurate and reliable data to work with. Data Can be in the format of text, video, images, audio and so on and it can be collected from carious source like the interest, journals, newspapers and so on.

Data Sources

Data Acquisition Sources Project Cycle

Data Exploration

Data Exploration is the process of arranging the gathered data uniformly for a better understanding. Data can be arranged in the form of a table, plotting a chart or making database.

If we simplify this Data Exploration means that the data which we collected in Data Acquisition, in Data Exploration we need to arrange it for example if we have data of 50 students in a class, we have their Mobile Number, Date of Birth, Class, Etc .

In the process of data exploration we can make a chart for that data in which all the names will be at one place and all the mobile numbers at one etc.

Data Exploration or Visualization Tools


Data Exploration Tools Project Cycle


1. Google Charts

Google chart tools are powerful, simple to use, and free. Try out our rich gallery of interactive charts and data tools.

2. Tableau

Tableau is often regarded as the grand master of data visualization software and for good reason.

Tableau has a very large customer base of 57,000+ accounts across many industries due to its simplicity of use and ability to produce interactive visualizations far beyond those provided by general BI solutions.

3. FusionCharts

This is a very widely-used, JavaScript-based charting and visualization package that has established itself as one of the leaders in the paid-for market.

It can produce 90 different chart types and integrates with a large number of platforms and frameworks giving a great deal of flexibility.

4. Highcharts

A simple options-structure allows for deep customization, and styling can be done via JavaScript or CSS. Highcharts is also extendable and pluggable for experts seeking advanced animations and functionality.

Modelling

Modelling is the process in which different models based on the visualized data can be created and even checked for the advantages and disadvantages of the model.

To Make a machine learning model there are 2 ways/Approaches Learning Based Approach and Rule Based Approach

Learning Based Approach

learning based approach Project Cycle

Learning Based Approach is based on Machine learning experience with the data feeded.

Machine Learning

Machine learning is a subset of artificial Intelligence (AI) which provides machines the ability to learn automatically and improve from experience without being programmed for it.

Types of Machine Learning

Machine learning can be divided into 3 types, Supervised Learning, Unsupervised Learning, andSemiSupervised or Reinforcement Learning

machine learning types Project Cycle

Supervised Learning

supervised learning is where a computer algorithm is trained on input data that has been labeled for a particular output example: a shape with three sides is labelled as a traingle, Classification and Regression models are also type of supervised Learning

Supervised Learning Project Cycle

What is classification ?

classification in which algorithm’s job is to separate the labelled data to predict the output. example: to predict weather which of them is apple and pineapple.

What is Regression ?

Regression is a type of supervised learning which is used to predict continuos value. example: Regression is used to predict wether . it is also used widely for wether forecasting .

Unsupervised Learning

In terms of machine learning, unsupervised learning is in which a system learn through data-sets created by its own. In this the training is not labeled.

Learning by own is termed as Unsupervised learning.
Basically, in unsupervised learning where the data is un-tagged or un-named, the machine create a learning algorithm using its structural data-sets present in its input.

Example: Suppose a boy sees someone performing tricks by a ball, so he also learnt the tricks by himself. This is what we call as unsupervised learning.

Reinforcement Learning

Learning through feed-back or trial and error method is called Reinforcement learning.

In this type of learning, The system works on Reward or Penalty policy. In this a agent perform a action positive or negative, in the environment which is taken as input from the system, then the system changes the state in the environment and the agent is provided with a reward or penalty.
The system also builds a policy, that what action should be taken under a specific condition.

Example: A very good example of these is Vending machines.
Suppose you put a coin (action) in a Juice Vending machine(environment), now the system detects the amount of coin give(state) you get the drink corresponding to the amount(reward) or if the coin is damaged or there is any another problem, then you get nothing (penalty).
Here the machine is building a policy that which drink should be provided under what condition and how to handle a error in the environment.

Indirect Impact SDG

Rule Based Approach

  • Rule Based Approach Refers to the AI modelling where the relationship or patterns in data are defined by the developer.
  • That means the machine works on the rules and information given by the developer and performs the task accordingly.
SDG

For example: Suppose have a datasets containing 100 images of apples and bananas each. Now you created a machine using Computer-Vision, and trained it with the labelled images of apples and bananas. If you test your machine with a image of an apple it will give you the output by comparing the images in its datasets. This is known as Rule Based Approach.

To know in detail visit Decision-tree

Datasets

Dataset is a collection of related sets of Information that is composed of separate elements but can be manipulated by a computer as a unit.

Supervised Learning Project Cycle

In Rule based Approach we will deal with 2 divisions of dataset:

1. Training Data - A subset required to train the model
2. Testing Data - A subset required while testing the trained the model

Training vs Testing Data


Base Training Set Testing Set
Use Used for Training the Model Used for Testing the Model after it is trained
Size Is allot bigger than testing data and constitutes about 70% to 80% It is smaller than Training Set and constitutes about 20% to 30%

Evaluation

Evaluation is the method of understanding the reliability of an API Evaluation and is based on the outputs which is received by the feeding the data into the model and comparing the output with the actual answers.

API Stands for Application Programming Interface