Artificial Intelligence
Master CBSE Class 11 Artificial Intelligence (Code 843) with Python, machine learning, and a hands-on capstone project.
A complete preparation course for CBSE Class 11 Artificial Intelligence (Subject Code 843), aligned to the official CBSE Department of Skill Education curriculum. Students learn AI foundations, Python programming, data literacy, and machine learning algorithms, then apply them in a real-world capstone project. The course follows the 50-mark theory plus 50-mark practical assessment scheme prescribed by CBSE.
What you'll learn
- ✓Explain core AI concepts, types, and domains, and discuss the benefits and limitations of AI
- ✓Write Python programs using tokens, operators, data types, and control statements
- ✓Use NumPy, Pandas, and Scikit-learn for data handling and basic AI tasks
- ✓Collect, pre-process, analyse, and visualize data using statistics and Matplotlib
- ✓Apply machine learning algorithms — Linear Regression, k-Nearest Neighbour, and k-Means Clustering
- ✓Build a simple NLP chatbot and understand sentiment analysis and the phases of NLP
- ✓Apply Design Thinking, empathy maps, and SDGs to plan and develop a capstone project
- ✓Recognize AI bias and ethical issues and apply strategies for fair, responsible AI
Full syllabus
Mapped to the official CBSE curriculum.
01Part A — Employability Skills+
- Unit 1: Communication Skills III
- Unit 2: Self-Management Skills III
- Unit 3: ICT Skills III (Basic Information and Communication Technology Skills)
- Unit 4: Entrepreneurial Skills III
- Unit 5: Green Skills III
02Unit 1: Introduction — Artificial Intelligence for Everyone+
- What is Artificial Intelligence?
- Evolution of AI
- Types of AI
- Domains of AI (Data, Computer Vision, Natural Language Processing)
- AI Terminologies
- Benefits and limitations of AI
03Unit 2: Unlocking Your Future in AI+
- The Global Demand for AI
- Common Job Roles in AI
- Essential Skills and Tools for Prospective AI Careers
- Opportunities in AI across Various Industries
04Unit 3: Python Programming+
- Level 1: Basics of Python, character sets, tokens, modes, operators, data types
- Level 1: Control statements (selective and iterative)
- Level 2: CSV files
- Level 2: Libraries — NumPy
- Level 2: Libraries — Pandas
- Level 2: Libraries — Scikit-learn
05Unit 4: Introduction to Capstone Project+
- Design Thinking methodology
- Empathy Map
- Problem decomposition using the 5W1H method
- Sustainable Development Goals (SDGs)
- Capstone Project (problem definition, users, brainstorming, design)
06Unit 5: Data Literacy — Data Collection to Data Analysis+
- What is Data Literacy?
- Data Collection and Exploring Data
- Statistical Analysis of data (mean, median, mode, standard deviation, variance)
- Representation and visualization of data with Python (Matplotlib)
- Introduction to Matrices and operations
- Data Pre-processing
- Data in Modelling and Evaluation
07Unit 6: Machine Learning Algorithms+
- Machine Learning in a nutshell and types of Machine Learning
- Supervised Learning
- Correlation, Regression and Linear Regression algorithm
- Classification and k-Nearest Neighbour (k-NN) algorithm
- Unsupervised Learning
- Clustering and k-Means Clustering algorithm
08Unit 7: Leveraging Linguistics and Computer Science+
- Understanding Human Language Complexity
- Introduction to Natural Language Processing (NLP)
- Emotion Detection, Sentiment Analysis and Classification Problems
- Chatbots
- Phases of NLP
- Applications of NLP
09Unit 8: AI Ethics and Values+
- Ethics in Artificial Intelligence
- The five pillars of AI Ethics
- Bias, bias awareness and sources of bias
- Mitigating Bias in AI Systems
- Developing AI Policies
- Moral Machine Game and Survival of the Best Fit Game
Tools you'll use
Exam pattern
Total 100 marks: Theory 50 marks + Practical 50 marks. Theory paper covers Part A (Employability Skills, 10 marks) and Part B (Subject-Specific Skills, 40 marks). Practical 50 marks = Practical File (10) + Written Exam based on practical file (10) + Viva Voce (6) + Capstone Project (12) + IBM SkillsBuild/industry certification (5) + Bootcamp/Internship (7).
Practical / project
Practical work includes a Practical File with at least one activity from each unit, one IBM SkillsBuild (or other industry) certification, and a bootcamp/internship participation certificate. Activities include Python programs for statistics and visualization, demonstrations of Linear Regression, k-NN, and k-Means, Pearson correlation in Excel, and building an ice-cream ordering chatbot. A Capstone Project (problem definition, users, brainstorming, and design completed in the IBM Log Book) is a major component, assessed alongside a written exam and viva voce.
Who it's for
CBSE Class 11 students who have opted for Artificial Intelligence (843) as a skill subject and want structured coverage of both theory and practical work, including beginners with no prior programming experience.
What's included
- ✓Live interactive online classes with Kajal Ma'am (teaching since 2006), conducted over shared screen for real-time concept building and live Python coding
- ✓Chapter-wise handwritten and typed notes covering every Code 843 unit, including Python Programming, Data Literacy, Machine Learning and AI Ethics
- ✓Step-by-step solutions to NCERT/CBSE textbook and handbook questions, plus the Employability Skills units
- ✓Regular topic-wise assignments and practice worksheets to reinforce theory and coding logic
- ✓Dedicated doubt-solving sessions so no question is carried forward to the next chapter
- ✓CBSE board paper and sample-paper practice with marking-scheme-based answer writing for the 50-mark theory exam
- ✓Hands-on practical and Capstone Project guidance using Python, NumPy, Pandas and Scikit-learn for the 50-mark practical component
- ✓Practical file and viva preparation aligned to the CBSE practical assessment format
Why study Artificial Intelligence?
CBSE Class 11 Artificial Intelligence (Code 843) is a scoring 100-mark elective split into 50 marks theory and 50 marks practical, which means consistent classwork and project effort can translate directly into a strong percentage. More importantly, Class 11 builds the conceptual base, Python fluency and machine-learning intuition that Class 12 AI and the board capstone project depend on, so getting it right now prevents gaps later. With AI literacy fast becoming a core skill, this subject gives students a genuine, hands-on understanding of how data and algorithms actually work rather than surface-level familiarity. A solid foundation here also makes related streams like Computer Science and Informatics Practices noticeably easier.
The Python programming, data literacy and machine-learning fundamentals in this course map directly onto entry-level skills for AI, data science, software engineering and analytics roles, and onto undergraduate programmes in Computer Science, AI/ML, and Information Technology. Students gain early exposure to the actual tools (Python, NumPy, Pandas, Scikit-learn) used in industry and higher study. This early start makes university coursework and self-driven projects considerably more approachable.
Learn directly from Kajal Ma'am
An MCA who has taught computer subjects since 2006, Kajal Mehta personally mentors every batch — turning dense theory into clear, exam-ready understanding.
Course FAQs
Are the classes live or recorded?+
Is this course offered in group batches or one-to-one?+
Is the course aligned to the CBSE Class 11 AI syllabus (Code 843)?+
Will I get help with the practical exam and Capstone Project?+
Who can join this course?+
Can I attend a demo class before enrolling, and what is your track record?+
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