Class 10 AI Chapter - Statistical Data Topic - No Code AI - Arvindzeclass

Class 10 Artificial Intelligent Code 417 Solutions

Session 2025-26

Artificial Intelligence code 417 syllabus pdf class 10 solutions. Class 10 AI Book. Part - A, Part - B, and Python. This article provides complete solution for class 10 AI (Artificial Intelligence) Code - 417 solution according to new syllabus 2025 – 26. In this article first of all go through the AI Code - 417 syllabus and then follow chapter wise notes. Class 10 AI MCQs Quiz with answers.

Part A - Employability Skills Notes

Chapter 1 - Communication Skills - II

Chapter 2 - Self Management Skills - II

Chapter 3 - ICT Skill - II

Chapter 4 - Entrepreneurial Skills - II

Chapter 5 - Green Skills - II

Part B - Subject Specific Skills Notes

Chapter 1 -AI Project Cycle & Ethics

Chapter 2 - Modelling in AI

Chapter 3 - Evaluating Models

Chapter 4 - Statistical Data

Chapter 5 - Computer Vision

Chapter 6 - Natural Language Processing

Chapter 7 - Advance Python

No code AI tools

1. Azure Machine Learning (Microsoft)

Type: Low-Code / No-Code AI Platform
Website: azure.microsoft.com

What it does:
Azure ML lets users build, train, and deploy machine learning models without deep coding. It offers a visual designer (drag-and-drop interface) for creating AI pipelines.

Key Features:

Drag-and-drop model builder (no code needed).
AutoML (automatically selects best algorithm).
Integration with Microsoft Power BI and Excel.
Cloud-based for scalability.

Who can use it:
Data analysts, IT professionals, or businesses wanting to deploy AI solutions.

Example Use:
Predicting customer churn or analyzing sales data using prebuilt models.

2. Google Cloud AutoML

Type: No-Code / Low-Code AI Platform
Website: cloud.google.com/automl

What it does:
Google Cloud AutoML allows users to train custom machine learning models without writing code — just by uploading data.

Key Features:

Supports image, text, video, and tabular data.
Uses Google’s deep learning technology.
Automatically optimizes models.
Integrates easily with Google Drive, Sheets, and Cloud Storage.

Who can use it:
Educators, researchers, small businesses, and enterprises who need quick AI models.

Example Use:
Training a model to classify images (like identifying types of plants or animals).

3. Lobe AI (by Microsoft)

Type: Pure No-Code AI Tool
Website: lobe.ai

What it does:
Lobe AI is a completely visual tool that helps anyone train image classification models. You just drag in pictures, label them, and train your model — no coding at all.

Key Features:

Extremely simple interface.
Real-time training and testing.
Exports models for use in apps or websites.
Works offline on your computer.

Who can use it:
Students, teachers, small businesses, or anyone exploring AI.

Example Use:
Creating an app that identifies fruits, animals, or handwriting from images.

4. Teachable Machine (by Google)

Type: No-Code AI Learning Tool
Website: teachablemachine.withgoogle.com

What it does:
Teachable Machine lets anyone train an AI model using images, sounds, or poses directly in their web browser — in just a few minutes.

Key Features:

Runs in browser — no software installation.
Supports image, audio, and pose detection.
Models can be exported to TensorFlow, Python, or JavaScript.
Ideal for education and quick experiments.

Who can use it:
Students, teachers, creators, and hobbyists learning about AI.

Example Use:
Training a model to recognize hand gestures or animal sounds.

5. Orange Data Mining

Type: No-Code / Visual Programming Tool for Data Science
Website: orange.biolab.si

What it does:
Orange is an open-source data visualization and analysis tool that lets users design workflows for machine learning through a drag-and-drop interface.

Key Features:

Visual workflow builder for data analysis.
Includes widgets for classification, regression, and clustering.
Supports data visualization (charts, scatter plots, etc.).
Integrates with Python for advanced users.

Who can use it:
Students, data analysts, and researchers.

Example Use:
Visualizing customer data to find patterns or training models to predict outcomes.

Summary Table

Tool	Type	Best For	Main Use
Azure Machine Learning	Low-Code	Businesses & Data Teams	Enterprise-level AI modeling
Google Cloud AutoML	No/Low-Code	Developers & Researchers	Training custom ML models easily
Lobe AI	No-Code	Beginners & Educators	Image classification
Teachable Machine	No-Code	Students & Creators	Quick image/audio/pose models
Orange Data Mining	No-Code	Data Analysts & Students	Data visualization & model building

Important concepts of Statistics

1. Population

Definition:
A population in statistics refers to the entire group of individuals, items, or data points that you want to study or draw conclusions about.

Example:

All students in a school.
All citizens of India.
All manufactured bulbs in a factory.

Key Point:
Studying the entire population is often difficult or time-consuming, so we use a sample instead.

2. Statistical Sampling

Definition:

Sampling is the process of selecting a small group (sample) from the entire population to study and make inferences about the population.

Example:

Instead of surveying all 10,000 students in a city, you select 500 students as a sample to represent the whole group.

Types of Sampling:

Random Sampling – every individual has an equal chance of being selected.
Systematic Sampling – selecting every kth person (e.g., every 10th student).
Stratified Sampling – dividing the population into groups (like male/female) and sampling from each.
Cluster Sampling – dividing into clusters (like schools or districts) and sampling entire clusters.

Why it’s important:
Sampling saves time, cost, and effort, while still giving accurate estimates about the population.

3. Descriptive Statistics

Definition:
Descriptive statistics are methods used to summarize and describe the main features of a dataset.
It helps us understand data through numbers, tables, and graphs.

Example:
If you collect test scores of 100 students, descriptive statistics help you summarize them as:

Mean (average)
Median (middle value)
Mode (most frequent value)
Range, Variance, Standard Deviation (to show spread)
Graphs like bar charts, pie charts, or histograms.

Purpose:
To give a clear picture of the data before moving to deeper analysis or predictions.

Main Types of Descriptive Statistics

1. Measures of Central Tendency

These tell us the center or average of the data.

Measure	Meaning	Example
Mean	The average of all values.	Test scores: 10, 20, 30 → Mean = (10+20+30)/3 = 20
Median	The middle value when data is arranged in order.	5, 8, 12 → Median = 8
Mode	The most frequently occurring value.	2, 4, 4, 5, 6 → Mode = 4

2. Measures of Dispersion (Spread)

These show how spread out or consistent the data is.

Measure	Meaning	Example
Range	Difference between largest and smallest value.	80 – 20 = 60
Variance	Shows how far values are from the mean.	Larger variance → more spread out data
Standard Deviation (SD)	The square root of variance — shows average distance from the mean.	Small SD = data close to mean

3. Measures of Position

These describe the location of values within a dataset.

Percentiles: Divide data into 100 equal parts (e.g., 90th percentile = higher than 90% of values).
Quartiles: Divide data into 4 equal parts (Q1, Q2, Q3, Q4).

4. Data Visualization

Descriptive statistics are often presented visually:

Bar charts – show categories.
Pie charts – show proportions.
Histograms – show frequency of numeric data.
Box plots – show spread and outliers.

Distribution

A distribution in statistics shows how data values are spread out or arranged.
It tells us how frequently different values occur in a dataset.

We often represent a distribution using graphs such as:

Histograms
Frequency polygons
Bell curves

So, a distribution helps us see:

Where most data points lie (center)
How spread out the data is (spread)
Whether it leans more to one side (skewness)

Types of Data Distributions

1. Normal Distribution (Symmetrical)

The data is evenly distributed around the mean.
The mean = median = mode.
The graph looks like a bell-shaped curve.
The graph looks like a bell-shaped curve.
Most values are close to the mean, and fewer are far away.

Example:
Heights, IQ scores, or test marks of a large population often follow a normal distribution.

2. Left-Skewed Distribution (Negatively Skewed)

The tail is on the left side.
Most values are high, with a few low outliers pulling the mean left.
Mean < Median < Mode

Example:
Age at retirement — most people retire around 60–65, but a few retire much earlier (lower tail).

3. Right-Skewed Distribution (Positively Skewed)

The tail is on the right side.
Most values are low, with a few very high values pulling the mean right.
Mean > Median > Mode

Example:
Income distribution — most people earn average or below-average income, but a few earn very high incomes (right tail).

Probability

1. Probability

Definition:
Probability is the measure of how likely an event is to occur.
It is expressed as a number between 0 and 1 (or 0% to 100%).

0 means the event will never happen.
1 means the event will definitely happen.

Formula:
$$[
P(E) = \frac{\text{Number of favorable outcomes}}{\text{Total number of outcomes}}
]$$

Examples:

Probability of getting a head when tossing a coin = 1/2 = 0.5
Probability of rolling a 3 on a die = 1/6 ≈ 0.1667

2. Variance

Definition:
Variance measures how much the data points differ from the mean.
It shows how spread out the data is.

Formula:
$$[
\text{Variance} = \frac{\sum (x_i - \bar{x})^2}{N}
]$$

where:
$ (x_i) = each data value $
$ (\bar{x}) = mean of the data $
(N) = total number of data points

Example:
Data: 2, 4, 6, 8
Mean = 5
Variance = [(2−5)² + (4−5)² + (6−5)² + (8−5)²] / 4 = 5

👉 Larger variance = data is more spread out.
👉 Smaller variance = data is tightly clustered around the mean.

3. Standard Deviation

Definition:
Standard Deviation (SD) is the square root of variance.
It tells how far, on average, each value is from the mean.

Formula:
$$[
SD = \sqrt{\text{Variance}}
]
$$

Example:
If variance = 5, then
$$[
SD = \sqrt{5} = 2.24
]$$

Interpretation:

Low SD: Data points are close to the mean.
High SD: Data points are widely spread.

In a normal distribution:

About 68% of data lies within ±1 SD from mean
About 95% within ±2 SD
About 99.7% within ±3 SD

4. Outlier

Definition:
An outlier is a data point that is much higher or lower than the rest of the data.
It does not fit the pattern of the dataset.

Example:
Test scores: 45, 50, 52, 53, 98
→ 98 is an outlier because it’s far away from others.

Why outliers matter: