In order to upgrade the skills and proficiency of the young generation and also to provide them awareness to explore about various career options the Central Board of Secondary Education has started offering 17 Vocational Courses in different sectors at Secondary level. The skill education envisions imparting the procedural knowledge and skills to the students that will enable students to excel and emerge successful in real situation of both work and life.
It works towards imparting an education that is holistic, meaningful and skill oriented which instills among the youth a sense of usefulness and responsibility. At Secondary Level, vocational subject is offered as additional sixth subject along with the existing five academic subjects.
If any student fails in any one of the three elective subject (Science, Mathematics and Social Science), then it will be replaced by the vocational subject (offered as a 6th additional subject) and result of Class X will be computed based on best five subjects. However, if a candidate desires to reappear in the failed subject, he or she may appear along with the compartment examination.
Part B which is subject specific skills has seven units: (i) Introduction to Artificial Intelligence (AI), (ii) AI Project Cycle, (iii) Advance Python, (iv) Data Science, (v) Computer Vision, (vi) Natural Language Processing, and (vii) Evaluation.
Part B: Subject Specific Skills
- Unit 1: Introduction to Artificial Intelligence (AI)
- Unit 2: AI Project Cycle
- Unit 3: Advance Python (To be assessed in Practicals only)
- Unit 4: Data Science (To be assessed in Practicals only)
- Unit 5: Computer Vision (To be assessed in Practicals only)
- Unit 6: Natural Language Processing
- Unit 7: Evaluation
Part C: Practical Work
- Unit 3: Advance Python
- Unit 4: Data Science
- Unit 5: Computer Vision
Unit 1: Introduction to Artificial Intelligence (AI)
Foundational concepts of AI
Session: What is Intelligence?
Session: Decision Making.
- How do you make decisions?
- Make your choices!
Session: what is Artificial Intelligence and what is not?
Basics of AI: Let’s Get Started
Session: Introduction to AI and related terminologies.
- Introducing AI, ML & DL.
- Introduction to AI Domains (Data, CV & NLP)
Session: Applications of AI - A look at Real-life AI implementations
Session: AI Ethics
Unit 2: AI Project Cycle
Introduction
Session: Introduction to AI Project Cycle
Problem Scoping
Session: Understanding Problem Scoping & Sustainable Development Goals
Data Acquisition
Session: Simplifying Data Acquisition
Data Exploration
Session: Visualising Data
Modelling
Session: Introduction to modelling
- Introduction to Rule Based & Learning Based AI Approaches
- Introduction to Supervised Unsupervised & Reinforcement Learning Models
- Neural Networks
Evaluation
Session: Evaluating the idea!
Unit 3: Advance Python
(To be assessed in Practicals only)
Recap
Session: Jupyter Notebook
Session: Introduction to Python
Session: Python Basics
Unit 4: Data Science
(To be assessed in Practicals only)
Introduction
Session: Introduction to Data Science
Session: Applications of Data Science
Session: Revisiting AI Project Cycle
Concepts of Data Sciences
Session: Python for Data Sciences
Session: Statistical Learning & Data Visualisation
K-nearest neighbour model
Activity: Personality Prediction
Session: Understanding K-nearest neighbour model
Unit 5: Computer Vision
(To be assessed in Practicals only)
Introduction
Session: Introduction to Computer Vision
Session: Applications of CV
Concepts of Computer Vision
Session & Activity: Understanding CV Concepts
- Pixels
- How do computers see images?
- Image Features
OpenCV
Session: Introduction to OpenCV
Hands-on: Image Processing
Convolution Operator
Session: Understanding Convolution operator
Activity: Convolution Operator
Convolution Neural Network
Session: Introduction to CNN
Session: Understanding CNN
- Kernel
- Layers of CNN
Activity: Testing CNN
Unit 6: Natural Language Processing
Introduction
Session: Introduction to Natural Language Processing
Session: NLP Applications
Session: Revisiting AI Project Cycle
Chatbots
Activity: Introduction to Chatbots
Language Differences
Session: Human Language VS Computer Language
Concepts of Natural Language Processing
Hands-on: Text processing
- Data Processing
- Bag of Words
- TFIDF
- NLTK
Unit 7: Evaluation
Introduction
Session: Introduction to Model Evaluation
Confusion Matrix
Session & Activity: Confusion Matrix
Evaluation Score Calculation
Session: Understanding Accuracy, Precision, Recall & F1 Score
Activity: Practice Evaluation
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