Textbook Solutions: Statistical Data | Artificial Intelligence for Class 10 PDF Download

Test Yourself

Q1: Orange data mining is an example of
a) Custom coding
b) Low code
c) No code
d) None of these
Ans: c) No code

Explanation: Orange is a no-code tool because it allows users to perform data analysis, machine learning, and visualization through a visual interface without writing any programming code.

Q2: Select which is not the feature of No code approach.
a) Visual
b) Highly expensive
c) Code free
d) Drag and drop
Ans: b) Highly expensive

Explanation: No-code tools are generally cost-effective and designed to be accessible. Their key features include visual design, drag-and-drop interfaces, and being code-free. “Highly expensive” is not a typical feature.

Q3: Assembly relies on developers to write and deploy code.
a) High code
b) Low code
c) No code
d) None of these
Ans: a) High code

Explanation: High-code approaches require developers to write most or all of the code manually. This offers maximum flexibility but is resource-intensive.

Q4: Flexibility is often limited in
a) High code
b) High code and Low code
c) No code
d) High code and No code
Ans: c) No code

Explanation: No-code platforms provide ease of use and speed but have limited flexibility for complex customizations, as users are restricted to the built-in features of the platform.

Q5: The organisation is heavily dependent on developer resources. This statement is true for
a) High code approach
b) Low code approach
c) No code approach
d) All of the above
Ans: a) High code approach

Explanation: High-code solutions require extensive developer involvement to write, maintain, and deploy applications. Low-code and no-code approaches reduce dependence on developers.

Reflection Time

1. Name any two-cloud based No-code AI tools?

Ans: The two-cloud based No-code AI tools are Azure Machine Learning and Google Cloud AutoML.

2. How accessible are no-code AI tools for non-technical users?

Ans: No-Code AI makes AI more accessible to the general public. Non-technical people such as doctors, architects, and musicians may quickly construct accurate AI models with no coding involved. This AI tools have user-friendly interface and prebuilt templates which helps to design the AI application. Thus, No-Code AI can empower individuals and organizations across various industries and skill levels to harness the potential of artificial intelligence for their specific needs.

3. What types of tasks can be accomplished with no-code AI tools?

Ans: The tasks that can be accomplished with no-code AI tools are text analysis, predictive analytics, and image recognition.

4. Can no-code AI tools be used for advanced projects? Justify.

Ans: Yes, No-code AI tools can used in advanced AI project with some limitation. No-code AI is basically used from faster development and deployment for non-technical users. No-code AI tolls have a pre trained models for the advanced project. No-Code AI is easy to use – even middle school students can create AI using No-Code tools. It has visual & drag-and-drop features, anyone can see what they are building in real-time

5. Do no-code AI tools require prior programming knowledge? Justify.

Ans: In No-Code AI, we can drag and drop, these models in few seconds. No coding knowledge is required to implement complex ML algorithms. Drag and drop feature of a No-Code tool makes it easier.

6. What are the benefits of using No-code AI tools?

Ans: The benefits of using no-code AI tools are –

• Accessibility – No-Code empowers non-technical makers to create websites and apps and also employ machine learning to solve business problems without programming.
• Fast – The speed at which no-code platforms enable you to build bespoke business solutions is significantly faster than traditional development.
• Easy to use – It includes drag-and-drop features that enable one to create an application with ease without any coding knowledge.
• Innovation – Since business users can now build solutions for their unique problems themselves, it creates a culture of innovation.

7. What are the challenges faced in using the No-code AI tools?

Ans: The challenges faced using the no-code AI tools are –

• Lack of Flexibility – Drag-and-drop elements can be very convenient. On the other hand, you are limited to those fixed elements.
• Automation Bias – Automation bias is the tendency for humans to favor suggestions from automated decision-making systems and to ignore contradictory information made without automation, even if it is correct.
• Security Issues – No code platforms do not essentially force you to think of security first or even evaluate security best practices. Therefore, these applications are only best suited for companies that don’t deal with sensitive data.

7. Differentiate low code and No-code AI tools with examples.

Ans: The difference between Low-code and No-code AI tools are –

8. As the CEO of a small e-commerce startup, you’re eager to leverage artificial intelligence to enhance your platform’s user experience and drive sales. However, your team lacks the technical expertise to develop and deploy AI-powered solutions. What would be your recommendations for the CEO?

Ans: The company can use no-code AI tools which can helps to create AI based platform for user experience. Some of the strategic approaches to be implemented are

• Find the key areas where AI can add values like chatbots for customer support or AI powered search etc.
• Use No-code AI tools like Azure Machine Learning application or Google Cloud AutoML.
• Invest in training for the employee, which can help the company for creating more flexible app.

9. Samarth attended a seminar on Artificial Intelligence and has now been asked to write a report on his learnings from the seminar. Being a non-technical person, he understood that the AI enabled machine uses data of different formats in many of the daily based applications but failed to sync it with the right terminologies and express the details. Help Samarth define Artificial Intelligence, list the three domains of AI and the data that is used in these domains.

Ans: Artificial intelligence (AI) is the ability of machines to do cognitive tasks such as thinking, perceiving, learning, problem-solving, and decision-making. ML and DL is a subset of Artificial Intelligence. The three domain of AI is –

• Statistical Data – is a domain of AI related to data systems and processes, in which the system collects numerous data, maintains data sets and derives meaning/sense out of them.
• Computer Vision – is an AI domain works with videos and images enabling machines to interpret and understand visual information and afterwards predict some decisions about it.
• Natural Language Processing – is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language.

Test Yourself

Q1: What type of tool is Orange?
a) Open-source
b) Closed-source
c) Paid software
d) Hardware
Ans: a) Open-source

Explanation: Orange is an open-source data visualization and machine learning tool, which means it is freely available for use, modification, and distribution.

Q2: Which of the following tasks can be performed using Orange?
a) Classification
b) Regression
c) Clustering
d) All of the above
Ans: d) All of the above

Explanation: Orange supports multiple machine learning tasks including classification (predicting categories), regression (predicting continuous values), and clustering (grouping similar data points).

Q3: What type of data can be imported into Orange?
a) CSV
b) Excel
c) SQL databases
d) All of the above
Ans: d) All of the above

Explanation: Orange can import data from various sources including CSV files, Excel spreadsheets, and SQL databases, providing flexibility for data analysis and model training.

Q4: What does the Data Table widget in Orange primarily facilitate?
a) Loading and viewing datasets
b) Performing clustering analysis
c) Running machine learning algorithms
d) Evaluating
Ans: a) Loading and viewing datasets

Explanation: The Data Table widget allows users to load datasets and inspect them in a tabular format. This is helpful for understanding the structure and contents of the data before applying machine learning.

Q5: Which component in Orange enables users to evaluate the performance of machine learning models?
a) Test & Score
b) Data Table
c) Data Projection
d) Data Exploration
Ans: a) Test & Score

Explanation: The Test & Score widget is used to evaluate machine learning models by calculating metrics like accuracy, precision, recall, and F1 score, helping users understand the effectiveness of their models.