Class 12 Artificial Intelligence Notes

Handling Missing Values (CBSE Class 12 Artificial Intelligence) Learn how to identify, remove, and replace missing values in datasets using the Pandas library to prepare clean and accurate data for Artificial Intelligence and Machine Learning.

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Importing and Exporting Data between CSV Files and DataFrames (CBSE Class 12 Artificial Intelligence) Learn how to import data from CSV files into Pandas DataFrames and export processed data back to CSV files for Artificial Intelligence and Data Science applications.

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Introduction to Python Programming-II (CBSE Class 12 Artificial Intelligence) Build upon your Class XI Python knowledge and discover how Python, along with powerful libraries like NumPy and Pandas, forms the foundation of Artificial Intelligence, Data Science, and Machine Learning.

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Linear Regression Algorithm (Advanced Learners) (CBSE Class 12 Artificial Intelligence) Learn the fundamentals of the Linear Regression algorithm, its working, implementation using Python, and how it is used to predict continuous values in Artificial Intelligence and Machine Learning.

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Practical: Import and Export Data between CSV Files and DataFrames (CBSE Class 12 Artificial Intelligence) Perform a complete practical to import data from a CSV file into a Pandas DataFrame, analyze the dataset, and export the processed data back to a CSV file using Python.

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Recap of NumPy Library (CBSE Class 12 Artificial Intelligence) Revise the fundamentals of the NumPy library, including arrays, dimensions, common functions, and its role in Artificial Intelligence, Data Science, and Machine Learning.

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Recap of Pandas Library (CBSE Class 12 Artificial Intelligence) Revise the Pandas library, its data structures, DataFrame operations, attributes, and its importance in Artificial Intelligence, Data Science, and Machine Learning.

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Introduction to Data Science Methodology (CBSE Class 12 Artificial Intelligence) Understand the concept of Data Science Methodology, its importance in solving real-world problems, and how a systematic approach helps build reliable Artificial Intelligence solutions.

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Model Performance Evaluation Metrics (CBSE Class 12 Artificial Intelligence) Learn the commonly used model performance evaluation metrics such as Confusion Matrix, Accuracy, Precision, Recall, F1-Score, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) used to evaluate Machine Learning models.

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Model Validation Techniques (CBSE Class 12 Artificial Intelligence) Learn the importance of model validation, different validation techniques, and how they help evaluate the performance and reliability of Machine Learning models before deployment.

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Practical: Calculate MSE and RMSE using Microsoft Excel (CBSE Class 12 Artificial Intelligence) Learn how to calculate Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) using Microsoft Excel to evaluate the performance of regression models.

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Practical: Calculate Precision, Recall, F1-Score and Accuracy from a Confusion Matrix (CBSE Class 12 Artificial Intelligence) Learn how to calculate Accuracy, Precision, Recall, and F1-Score from a Confusion Matrix using manual calculations and Microsoft Excel to evaluate the performance of classification models.

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Practical: Python Code to Evaluate a Machine Learning Model (CBSE Class 12 Artificial Intelligence) Learn how to evaluate a Machine Learning classification model in Python using Scikit-learn by generating a Confusion Matrix, Accuracy, Precision, Recall, and F1-Score.

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Steps of Data Science Methodology (CBSE Class 12 Artificial Intelligence) Learn the systematic steps of the Data Science Methodology used to solve real-world problems, from understanding the business problem to deploying and monitoring AI models.

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Applications of Computer Vision (CBSE Class 12 Artificial Intelligence) Explore the diverse applications of Computer Vision in healthcare, transportation, agriculture, education, security, manufacturing, retail, and many other industries that use Artificial Intelligence.

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Challenges of Computer Vision (CBSE Class 12 Artificial Intelligence) Understand the major challenges faced by Computer Vision systems, including lighting conditions, image quality, occlusion, viewpoint variation, large datasets, computational requirements, and ethical concerns.

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Computer Vision Process (CBSE Class 12 Artificial Intelligence) Understand the complete Computer Vision process, from capturing an image to making intelligent decisions, and learn how each stage contributes to accurate visual recognition.

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How Machines See (CBSE Class 12 Artificial Intelligence) Learn how computers capture, represent, and interpret images using pixels, digital image processing, and Machine Learning to recognize objects just like humans.

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Introduction to Making Machines See (CBSE Class 12 Artificial Intelligence) Understand how computers interpret visual information using Computer Vision, why machines need images as data, and how this technology powers modern Artificial Intelligence applications.

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Working of Computer Vision (CBSE Class 12 Artificial Intelligence) Learn how Computer Vision systems capture, process, analyze, and interpret images step by step using Artificial Intelligence and Machine Learning techniques.

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Beneficiaries of Orange Data Mining Explore how Orange Data Mining benefits data analysts, researchers, educators, students, business professionals and the open-source community through its accessible visual programming environment and powerful data analysis capabilities.

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Components of Orange Learn about the three main components of Orange Data Mining—Canvas, Widgets and Connectors-and understand how they work together to create visual data analysis workflows.

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Default Widget Catalogue Explore the Default Widget Catalogue in Orange Data Mining and understand how Data, Transform, Visualize, Model, Evaluate and Unsupervised widgets support different stages of data analysis and machine learning workflows.

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Getting Started with Orange Tool Learn how to get started with Orange Data Mining by downloading the software, selecting the appropriate version for Windows or Mac, installing it, and launching the tool to access tutorials, examples and documentation.

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Introduction to Orange Data Mining Tool Learn about Orange, a component-based visual programming tool used for data visualization, machine learning, data mining and data analysis, and understand how widgets can be connected to create AI workflows without extensive coding.

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Key Domains of AI with Orange Data Mining Tool Explore how Orange Data Mining Tool is used in three key AI domains—Data Science, Computer Vision, and Natural Language Processing (NLP)—and understand the major widgets, workflows, add-ons, and practical applications associated with each domain.

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What is Data Mining? Understand Data Mining as the process of discovering useful patterns, trends and information from large datasets, and learn how analysing data helps organisations make informed and effective decisions.

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Advantages and Disadvantages of Big Data Explore the major advantages and disadvantages of Big Data, including improved decision-making, efficiency, customer insights, innovation, privacy concerns, data quality, technical complexity, regulatory compliance and resource requirements.

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Big Data Analytics Understand Big Data Analytics, its purpose, four major types—Descriptive, Diagnostic, Predictive and Prescriptive Analytics—and the global trends such as Moore’s Law, Mobile Computing, Social Networking and Cloud Computing that drive Big Data Analytics.

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Characteristics of Big Data Learn the six key characteristics of Big Data—Volume, Velocity, Variety, Veracity, Value and Variability—and understand how each characteristic affects the collection, processing, analysis and usefulness of large datasets.

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Future of Big Data Analytics Explore the future of Big Data Analytics through real-time analytics, advanced predictive models and quantum computing. Understand how these emerging technologies can improve processing speed, forecasting accuracy and data-driven decision-making.

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Introduction to Big Data Understand the concept of Big Data, its meaning, major sources, and why specialised technologies are required to store, process, analyse, and extract useful insights from extremely large and complex datasets.

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Mining Data Streams Learn how continuous streams of real-time data are collected and analysed to discover meaningful patterns, trends and insights. Understand data streams, stream mining, its difference from traditional data mining, and real-world applications.

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Types of Big Data Learn about the three major types of Big Data—Structured, Semi-Structured, and Unstructured Data—with their characteristics, examples, differences, and real-world applications.

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Working on Big Data Analytics Learn the four major stages of Big Data Analytics—Gather Data, Process Data, Clean Data and Analyze Data—and understand Batch Processing, Stream Processing, data cleaning, and commonly used Big Data tools.

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Components of a Neural Network The main components of a neural network include neurons, weights, activation functions, bias, connections, learning rules and propagation functions. Together, these components enable a neural network to process information, learn from data and improve its predictions.

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Future of Neural Networks and Societal Impact Explore the future of neural networks, their role in advancing Artificial Intelligence, and their impact on efficiency, personalization, autonomous systems, employment, data privacy, and ethical decision-making.

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Parts of a Neural Network A neural network is organised into interconnected layers of artificial neurons. The three essential parts are the input layer, hidden layers and output layer, which together receive, process and produce information.

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Types of Neural Networks Learn about the major types of neural networks, including Perceptron, Feed Forward Neural Network, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Generative Adversarial Network (GAN), along with their structures, working principles, and real-world applications.

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What is a Neural Network? A neural network is a machine learning model inspired by the human brain that uses interconnected neurons arranged in layers to process data, identify patterns, and make predictions or decisions.

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Working of a Neural Network Understand how a neural network processes inputs using weights, bias and activation functions, and how forward propagation and back propagation help the network learn from errors and improve its predictions.

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Applications of Generative AI Explore how Generative AI is used to create text, images, videos and audio, along with applications in business, healthcare, education and creative fields.

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Ethical and Social Implications of Generative AI Generative AI offers powerful opportunities for creativity, education and productivity, but it also raises important ethical and social concerns including deepfakes, bias, plagiarism, copyright, privacy, misinformation, hallucinations and job displacement.

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Future of Generative AI Explore the future of Generative AI, including advances in language and multimedia generation, applications in healthcare and education, human-AI collaboration, and the importance of responsible AI development.

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Generative and Discriminative Models Understand the difference between generative and discriminative models, their training approaches, purposes, examples, and real-world applications.

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Introduction to Generative AI Understand the meaning, purpose and key characteristics of Generative AI, including how it learns patterns from existing data to create new text, images, audio and video.

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LLM – Large Language Model Understand Large Language Models (LLMs), their working, transformer architecture, applications, examples, limitations, and the role of LLMs in modern Generative AI.

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Working of Generative AI Understand how Generative AI learns patterns from large datasets and generates new content using deep learning architectures such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

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Elements of a Story A well-structured story consists of five essential elements: characters, plot and setting, conflict, resolution, and insights. These elements provide structure, context, purpose, and meaning to a narrative.

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Essential Elements of Data Storytelling Effective data storytelling combines three essential elements—Data, Narrative, and Visuals. Data provides the foundation, narrative gives context and meaning, and visuals help communicate patterns and insights clearly. Together, they can explain, enlighten, engage, and drive meaningful change.

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Ethics in Data Storytelling Ethical data storytelling requires accuracy, transparency and respect for privacy. Data should not be manipulated, narratives should disclose sources and limitations, and visualizations should avoid misleading representations or fallacies.

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Introduction to Data Storytelling Data storytelling combines data, narrative, and visuals to communicate insights in a clear, compelling, and relatable way. It transforms complex data into meaningful information that helps audiences understand findings and make informed decisions.

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Introduction to Storytelling Storytelling is the art of sharing factual or fictional events through oral, written, or digital forms. It connects people, communicates ideas, simplifies complex concepts, captures attention, and inspires action.

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Narrative Structure of a Data Story (Freytag’s Pyramid) Freytag’s Pyramid is a five-stage narrative structure used to organise a story from introduction to conclusion. In data storytelling, it guides the audience from the background and rising insights to the key “Aha! Moment” and finally towards solutions and next steps.

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Steps to Create a Story Through Data A data story is created by collecting and organising relevant data, visualising it appropriately, identifying relationships and patterns, and developing a simple, crisp and contextual narrative from the insights.

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Types of Data and Visualizations for Different Data Data can be qualitative or quantitative, and structured, semi-structured, or unstructured. Different visualizations such as bar charts, line graphs, scatter plots, histograms, word clouds, maps, and heat maps help communicate data effectively.

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Why is Data Storytelling Powerful? Data storytelling is powerful because it transforms complex data into meaningful narratives that are easier to understand, remember, and communicate. It improves engagement, persuades audiences, bridges the gap between technology and people, and can inspire meaningful action and change.

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