artificial intelligence version 3.0 class 10

It sounds like you're looking for information about an "Artificial Intelligence Version 3.0" textbook or curriculum for Class 10. The most common and official textbook for AI in Indian schools (CBSE) for Class 10 is "Artificial Intelligence (417) Part B: Subject Specific Skills" . However, many publishers create their own editions, and the term "Version 3.0" is often used to indicate an updated edition that aligns with the latest CBSE Skill Education Curriculum (often referred to as the 2024-2025 or 2025-2026 syllabus). I cannot provide the exact content of a specific book from a specific publisher (like NCERT, Kips, Orange, etc.), as "Version 3.0" is a marketing term used by different publishers. However, I can give you a comprehensive overview of the topics you will study in a standard Class 10 AI curriculum (based on CBSE Skill Subject 417) , which is what any "Version 3.0" textbook would cover. Here is what you will typically learn in Part B: Subject Specific Skills for Class 10 AI: Unit 1: Introduction to AI (Revision & Deep Dive) What is AI? A quick recap of AI, Machine Learning, Deep Learning. Domains of AI: - Data Science: Collecting, processing, and analyzing data. - Computer Vision: How computers "see" and interpret images (e.g., face detection, object recognition). - Natural Language Processing (NLP): How computers understand and interact with human language (e.g., chatbots, translators). Applications of AI: In healthcare, education, smart homes, autonomous vehicles, etc. AI Ethics: Bias in data, privacy concerns, job displacement. Unit 2: AI Project Cycle This is the core of the Class 10 AI syllabus. You will learn the step-by-step process of building an AI project. A common mnemonic is "DADAS" : Problem Scoping: - Understanding the problem using the 4Ws Problem Canvas (Who, What, Where, Why). Data Acquisition: - Where to get data (e.g., surveys, sensors, websites). - Data Sources: Primary (collected by you) vs. Secondary (from existing sources). Data Exploration: - Visualizing data using tools like Python (Pandas, Matplotlib) or Google Colab. - Finding patterns, missing values, and outliers. Data Modelling: - Applying algorithms (e.g., Decision Trees, K-Nearest Neighbors) to train a model. - Training vs. Testing data (80/20 rule). Evaluation: - Checking how accurate your model is. - Understanding concepts like overfitting and underfitting. Unit 3: Neural Networks (Introduction) Perceptron: The basic building block of a neural network. Structure: Input layer, Hidden layer(s), Output layer. Activation Functions: A simple introduction to functions like ReLU or Sigmoid. How a Neural Network "learns": Forward propagation and backpropagation (conceptual understanding, not mathematical). Unit 4: AI Applications & Python (Practical) Python Basics: A refresher on variables, data types (lists, dictionaries), loops, and conditionals. Libraries: - NumPy: For arrays and mathematical operations. - Pandas: For data manipulation and analysis (creating DataFrames). - Matplotlib: For creating graphs (line plots, bar charts, scatter plots). Building a simple model: You will typically use pre-existing datasets to train a classifier (e.g., predicting if a person buys a product or not). Important Note for Your Studies: Don't just look for "Version 3.0." Instead, check for: CBSE Syllabus Code 417: This is the official code for the AI subject (Skill Subject). Current Academic Year: Make sure the book is for the 2024-2025 or 2025-2026 session. Practical Work: The Class 10 AI exam has a theory paper (50 marks) and a practical/ project component (50 marks). Your textbook should have practical exercises using Python (often on Google Colab or Jupyter Notebook). Where to find the exact content: NCERT Textbook: "Artificial Intelligence" for Class 10 (if released for your session). CBSE Academic Website: Download the latest Curriculum for Skill Subject 417. Publisher Websites: Kips, Orange Education, and others have online portals or sample PDFs for their "Version 3.0" books. If you have a specific chapter or topic in mind (e.g., "4Ws Problem Canvas" or "What is a Decision Tree?"), please tell me, and I can explain that concept in detail!