CBSE Class 12 Artificial Intelligence (843)

1. Part A: Employability Skills

Communication skills · Self-management skills · ICT skills · Entrepreneurial skills · Green skills

• Communication Skills: How to Share Meaning Clearly and Kindly – Communication is sharing meaning between people. A sender encodes an idea into a message, sends it through a channel, and a receiver decodes it and gives feedback. Barriers such as noise, hard words, strong emotions and cultural differences can block or bend the message. We send meaning with words (verbal), with face, eyes, gestures, posture and tone (non-verbal), and with pictures (visual). Active listening — attention, patience, questions and paraphrasing — proves the message arrived. Good communicators adapt to context: formal or informal, spoken or written, and use polite requests, kind refusals, sincere apologies and assertive (not aggressive or passive) language.
• Self-Management: Taking Charge of Your Feelings, Goals and Time – Self-management is the skill of guiding your own feelings, thoughts and actions so you can reach your goals. It starts with self-awareness: noticing what you feel and how strong it is. Then you pause before reacting (Stop, Think, Act) and use calming tools such as slow breathing. Your beliefs and thoughts shape your feelings and actions, so changing an unhelpful thought ('I'm useless') into a helpful one ('I can't do it yet') changes what you do. Good self-managers set SMART goals, plan their time by importance, build healthy habits and bounce back from setbacks (resilience). These skills help in school, friendships and future careers.
• ICT Skills: Office Tools, Internet, E-mail and Staying Safe – ICT means Information and Communication Technology. It is the set of tools we use to make, store and share information: computers, phones, software and the internet. The core ICT skills are: using a word processor to write, a spreadsheet to calculate, a presentation tool to show ideas, the internet to search, e-mail to communicate, and good habits to stay safe online.
• Green Skills: Sustainable Development, Green Economy and Green Jobs – Green skills are the knowledge, habits and job skills that help us live and work without harming the planet. They rest on sustainable development (meeting today's needs while leaving enough for the future). A green economy grows while cutting pollution and waste, often by going circular: make, use, repair, recycle. It creates green jobs such as solar technician or recycling worker. Everyone can use green skills through the 5 Rs: Refuse, Reduce, Reuse, Repair, Recycle.

2. Python Programming II

Python for data

• Python Revision for Class 12: Everything from Class 11 in One Place – Class 12 builds on Class 11 Python. You must know tokens (keywords, identifiers, literals, operators, punctuators), data types (int, float, bool, str, list, tuple, dict), which types are mutable (can change in place) and which are immutable, operator precedence, if-elif-else, for and while loops with break and continue, string slicing, and the main methods of lists, tuples and dictionaries.

3. Data Science Methodology

Data science methodology

• Data Science Methodology: From a Problem to a Working Model – Data science methodology is a step-by-step cycle for solving a problem with data. Its 10 stages are: business understanding (the problem), analytic approach, data requirements, data collection, data understanding, data preparation, modelling, evaluation, deployment and feedback. Data is split into training and test sets (or checked with k-fold cross-validation) so the model is tested on data it has not seen. Classification models are judged by accuracy; regression models by errors such as MSE and RMSE. Feedback from real use starts the cycle again.

4. Making Machines See

Computer vision

• Computer Vision: How Machines See – Computer vision (CV) is the part of AI that lets computers understand images and videos. A digital image is a grid of pixels; each pixel is a number (0–255 for grey) or three numbers (R, G, B) for colour. Resolution is width × height in pixels. CV finds features such as edges, corners and colours, then does tasks like classification (what), object detection (where) and segmentation (which pixels). It is used in face unlock, self-driving cars, medical scans, farming, shops and traffic cameras.

5. AI with Orange Data Mining Tool

No-code AI

• No-Code AI: Building Models with Orange Widgets – No-code AI tools let you build and test machine-learning models by dragging blocks instead of writing code. In Orange, each block is a widget: File loads data, Data Table and Scatter Plot show it, model widgets (kNN, Tree, Logistic Regression, Naive Bayes) learn from it, Test & Score measures accuracy using cross-validation or a train-test split, Confusion Matrix shows which classes get mixed up, and Predictions applies the model to new data. No-code tools are fast and friendly, but you still need clean data, a clear question and careful evaluation.

6. Big Data and Data Analytics

Big data

• Big Data – Big data is data that is too big, too fast or too mixed to store and process on one ordinary computer with normal tools. We describe it with Volume (how much), Velocity (how fast it arrives) and Variety (how many kinds). To handle it, the work is split across many machines that run at the same time (distributed processing, for example MapReduce). Big data is often stored as simple facts or as a graph of nodes and links, and it is used for weather forecasts, maps, health, shopping and training AI. It also raises questions about privacy and fairness.

7. Understanding Neural Networks

Neural networks

• Neural Networks and Deep Learning – A neural network is a computer model made of many small units called artificial neurons. Each neuron multiplies its inputs by weights, adds them with a bias and passes the sum through an activation function. Neurons are arranged in layers: input, hidden and output. The network learns by training: it makes a guess, measures the error with a loss function, and changes the weights backwards (backpropagation with gradient descent) so the error becomes smaller. Networks with many hidden layers are called deep learning. CNNs are good at images, RNNs at sequences like text and speech.

8. Generative AI

Generative AI

• Generative AI: How Machines Create Text, Images and More – Generative AI is artificial intelligence that creates new content (text, images, audio, video, code) after learning patterns from huge amounts of example data. A large language model writes one token at a time by giving each possible next token a probability and choosing one; temperature controls how adventurous that choice is. Image models such as diffusion models start from noise and remove it step by step, guided by a prompt; GANs pit a generator against a discriminator. Generative AI helps with writing, design, coding, translation and learning, but it can invent facts (hallucinate), repeat bias, enable deepfakes and raise privacy and copyright problems. Responsible use means checking facts, being open about AI use, protecting personal data and respecting creators.

9. Data Storytelling

Data storytelling

Coming soon