Class 10 Artificial Intelligent Code 417 Solutions
Session 2025-26
Artificial Intelligence code 417 syllabus pdf class 10 solutions. Class 10 AI Book. Class 10 AI Notes. This article provides complete solution for class 10 AI (Artificial Intelligence) Code - 417 students 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.
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Chapter - Modelling in AI
Other Topics
Relationship AI, ML & DL
1. Artificial Intelligence (AI)
Definition:
Artificial Intelligence is a broad field of computer science focused on creating machines that can mimic human intelligence.
Examples:
- Voice assistants like Siri and Alexa
- Chatbots
- Self-driving cars
- Face recognition systems
Goal:
To make machines "smart" — able to solve problems, understand language, recognize images, and make decisions.
2. Machine Learning (ML)
Definition:
Machine Learning is a subset of AI. It focuses on building systems that can learn from data and improve automatically without being explicitly programmed.
How it works:
It uses algorithms to find patterns in data and make predictions or decisions based on that.
Examples:
- Email spam filters
- Movie recommendations (like on Netflix)
- Stock price predictions
Artificial Intelligence
3. Deep Learning (DL)
Definition:
Deep Learning is a subset of Machine Learning that uses neural networks with many layers (called deep neural networks). It’s inspired by how the human brain works.
How it works:
It handles massive amounts of data and learns complex patterns — especially useful for image, audio, and natural language.
Examples:
- Face detection in photos
- Speech recognition
- Language translation
- ChatGPT itself uses deep learning!
Summary Table
| Concept | Part of | Focus | Example Use |
|---|---|---|---|
| Artificial Intelligence | Broadest field | Simulating human intelligence | Self-driving car |
| Machine Learning | Subset of AI | Learning from data | Fraud detection |
| Deep Learning | Subset of ML | Neural networks & big data | Face recognition |
Machine Learning
Machine Learning (ML) is a branch of Artificial Intelligence (AI) that allows computers to learn from data and make decisions or predictions without being explicitly programmed.
Instead of being told what to do step by step, a machine learning model learns patterns from examples (data) and improves its performance over time.
Key Idea:
"Learn from past data to make future decisions."
Five Applications of Machine Learning
Here are five practical applications of machine learning in everyday life:
 |
| Application of ML |
1. Email Spam Filtering
- What it does: Detects and filters out spam or junk emails.
- How ML helps: Learns from past spam emails and blocks similar messages in the future.
2. Recommendation Systems
- Example: Netflix, YouTube, Amazon
- What it does: Suggests movies, products, or songs based on your preferences.
- How ML helps: Analyzes your past behavior to recommend things you’re likely to enjoy.
3. Self-Driving Cars
- What it does: Allows vehicles to drive without human input.
- How ML helps: Learns from sensor data (camera, radar) to detect obstacles, follow traffic rules, and navigate roads.
4. Face Recognition
- Example: Phone unlocking, Facebook tagging
- What it does: Identifies or verifies a person from an image.
- How ML helps: Trains on thousands of faces to recognize new ones.
5. Medical Diagnosis
- What it does: Helps doctors detect diseases like cancer, diabetes, etc.
- How ML helps: Analyzes medical images and health data to assist in diagnosis and treatment planning.
Deep Learning
Deep Learning (DL) is a specialized subfield of Machine Learning that uses artificial neural networks with multiple layers to model complex patterns in large amounts of data.
Inspired by the human brain, deep learning can automatically learn features from data without manual feature selection — especially powerful for image, speech, and language tasks.
Key Characteristics:
- Uses neural networks
- Learns from large datasets
- Performs well on unstructured data (like images, audio, text)
Five Applications of Deep Learning
 |
| Application of DL |
1. Speech Recognition
- Use: Voice assistants (Siri, Google Assistant, Alexa)
- Deep Learning Role: Converts spoken language into text with high accuracy using Recurrent Neural Networks (RNNs) or Transformers.
2. Image Recognition
- Use: Face detection, medical imaging, security cameras
- Deep Learning Role: Convolutional Neural Networks (CNNs) can identify objects, faces, and patterns in images.
3. Natural Language Processing (NLP)
- Use: Translation (Google Translate), Chatbots (like ChatGPT), Sentiment Analysis
- Deep Learning Role: Models like Transformers (e.g., BERT, GPT) understand and generate human language.
4. Autonomous Vehicles
- Use: Self-driving cars
- Deep Learning Role: Detects objects, traffic signs, pedestrians using camera feeds to make real-time decisions.
5. Medical Diagnosis
- Use: Detecting diseases from X-rays, MRIs, CT scans
- Deep Learning Role: Assists doctors by analyzing images and patterns that are difficult for the human eye to spot.
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