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AI Project Cycle provides us with an appropriate framework which can lead us towards the goal. The AI Project Cycle mainly has 5 stages
Problem Statement Template (PST)
Problem Statement Template (PST)
The Problem Statement Template helps us to summarise all the key points into one single.
Surveys
Survey is one of the method to gather data from the users for the second stage of ai project cycle that is data acquisition.
Survey is a method of gathering specific information from a sample of people. for Example a census survey is conducted every year for analyzing the population.
Surveys are conducted in particular areas to acquire data from particular people.
Web Scraping
Web Scraping means collecting data from web using some technologies.
We use it for monitoring prices, news and etc.
For example using Programming we can Do webscrapping. using beutiful soup in python. beautiful soup is a package in Python
Sensors
Sensors are very Important but very simple to understand.
Sensors are the part of IOT. IOT is internet of things.
Example of IOT is smart watches or smart fire alarm which automatically detects wire and starts the alarm.. How does this happen, this happens when sensors like fire sensor sends data to the IOT or the smart alarm and if sensor detects heat or fire the alarm starts.
Cameras
Camera captures the visual information and then that information which is called image is used as a source of data.
Cameras are used to capture raw visual data.
Observations
When we observe something carefully we get some information
For example, Scientists take instects in observation for years and that data will be used by them . So this is a data source.
Observations is a time consuming data source.
API stands for Application Programming interface.
Let us take an example to understand API: When you visit a restaurant and check the menu, and then you want to order some food, do you do to the kitchen and ask the cook to prepare food, no right. You ask the waiter for the order and then the waiter gives that order to the main kitchen area.
So here waiter is a messenger which takes request and tells the kitchen what you want and then the waiter responds you with the food
Like that: API is actually a messenger which takes requests from you and then tells the system what you want and then it gives you a response.
Now the response which it takes can be in json format or other formats..
Well what is json .. it is just a format to store structured, object type data. Below is an example of JSON, for nerds.
What is Data Features
Data features refer to the type of data you want to collect. For example, data features would be salary amount, increment percentage, increment period, bonus, etc.
Data Exploration or Visualisation Tools
Data Exploration or Visualisation Tools
Google Charts
Google chart tools are powerful, simple to use, and free. Try out our rich gallery of interactive charts and data tools.
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.
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.
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.
For example, we have a dataset which tells us about the conditions on the basis of which we can decide if an elephant may be spotted or not while on safari. The parameters are: Outlook, Temperature, Humidity and Wind.
A drawback/feature for this approach is that the learning is static. The machine once trained, does not take into consideration any changes made in the original training dataset.
Learning Based Approach
Refers to the AI modelling where the machine learns by itself. Under the Learning Based approach, the AI model gets trained on the data fed to it and then is able to design a model which is adaptive to the change in data.
The unsupervised learning models are used to identify relationships, patterns and trends out of the data which is fed into it
Supervised Learning
In a supervised learning model, the dataset which is fed to the machine is labelled. A label is some information which can be used as a tag for data. For example, students get grades according to the marks they secure in examinations. These grades are labels which categorise the students according to their marks.
Two Types
a. Classification – Where the data is classified according to the labels. For example, in the grading system, students are classified on the basis of the grades they obtain with respect to their marks in the examination. This model works on discrete
dataset which means the data need not be continuous.
b. Regression – : Such models work on continuous data.For example, if you wish to predict your next salary, then you would put in the data of your previous salary, any increments, etc., and would train the model. Here, the data which has been fed to the
machine is continuous.
Unsupervised Learning
An unsupervised learning model works on unlabelled dataset. This means that the data which is fed to the machine is random and there is a possibility that the person who is training the model does not have any information regarding it .
For example, y you have a random data of 1000 dog images and you wish to understand some pattern out of it, you would feed this data into the unsupervised learning model and would train the machine on it. After training, the machine would come up with patterns which it was able to identify out of it.
There are two type of Unsupervised learning models in AI –
a. Clustering – refers to the unsupervised learning technique that can cluster the unknown data according to patterns or trends found in it. The developer may already be aware of the patterns noticed, or it may even generate some original patterns as a result.
b. Dimensionality Reduction – If you have a large number of features, it could be beneficial to minimise them using an unsupervised step before moving on to supervised steps. Numerous unsupervised learning techniques include a transform technique that can be used to lessen the conditionality.
Reinforcement Learning
Reinforcement Learning
In this type of learning, The system works on Reward or Penalty policy. In this an agent performs an 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 given (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 an error in the environment.
What do you mean by the neural network?
The neural network is a model that works as neurons works in human brains.
It is also known as ANN (Artificial Neural Networks)
It copies the mechanism as working in human brains.
It can extract the information without any code or programming.
In a neural network, the machine can learn, recognize and make decisions like human beings.
Where the neural network used?
A neural network is used in many fields where a large data set is required..
It can be used in the following:
Voice Recognition
Character Recognition
Signature Verification Applications
Human face recognition
How does the neural network work?
The input layers inputs or fed data
The hidden layers process these data and assign weight to each layer randomly
Every layer process has its own block which accomplishes the task and passes to the next layer.
Then it comes to the output layer where machine learning executes the data received from input layers.
Finally, the processed data passed to the output layer.
What are the features of a neural network?
The model of the neural network follows the mechanism of the human brain
It extracts information without any input from the user
It basically works on a mathematical way with machine algorithms
Suitable for large datasets
The top layers are input layers to fed data, hidden layers process the data, and the output layer generates output from the processed data
What is Sustainable Development?
Answer – When all renewable resources are utilized properly, the variety of life on earth is conserved, and environmental harm is kept to a minimum for the benefit of future generations, this is considered sustainable development.
According to the Bruntland Commission Report from 1987, sustainable development refers to “development that satisfies present demands without compromising the ability of future generations to meet their own needs.”
What are the goals of sustainable development?
Answer – There are 17 sustainable development goals announced by the United nations, aim to achieve these goals by the end of 2030 –
a. No Poverty
b. Zero Hunger
c. Good Health and Well-being
d. Quality Education
e. Gender Equality
f. Clean Water and Sanitation
g. Affordable and Clean Energy
h. Decent Work and Economic Growth
i. Industry, Innovation and Infrastructure
j. Reduced Inequality
k. Sustainable Cities and Communities
l. Responsible Consumption and Production
m. Climate Action
n. Life Below Water
o. Life on Land
p. Peace and Justice Strong Institutions
q. Partnerships to achieve the Goal
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