Artificial Intelligence Class XII Student Handbook

Acknowledgments

Patrons:

  • Mr. Rahul Singh, IAS, Chairperson, Central Board of Secondary Education
  • Dr. Biswajit Saha, Director (Skill Education), Central Board of Secondary Education
  • Sh. Ravinder Pal Singh, Joint Secretary, Department of Skill Education, Central Board of Secondary Education

Strategic Advisory:

  • Ms. Shipra Sharma, CSR Leader, India/South Asia, IBM
  • Dr. Mani Madhukar, Program Lead - SkillsBuild, IBM

Curriculum Planning Team:

  • Mr. Manav Subodh, Founder & Chief Mentor, 1M1B
  • Mr. Saffin Mathew, Programs Director, 1M1B

Lead Curriculum Curator:

  • Ms. Mehreen M Shamim, AI Curriculum Manager, 1M1B

AI Teacher Advisory for Curriculum Revision:

  • Mr. Akhil R, TGT, DAV Public School Pushpanjali Enclave, Delhi
  • Ms. Anni Kumar, PGT, Vikas Bharati Public School, Delhi
  • Ms. Harmeet Kaur, PGT, Vasant Valley School, New Delhi
  • Ms. Jyoti P B, PGT, Jyothis Central School, Kerala
  • Ms. Meena Bisht, TGT, Army Public School Noida
  • Mr. Naveen Gupta, PGT, St. Mark's Sr. Sec. Public School, Delhi
  • Ms. Neeru Mittal, PGT, Shaheed Rajpal DAV Public School, Delhi
  • Ms. Rani Kumari, PGT, DLF Public School, Ghaziabad
  • Ms. Roshy Thomas, PGT, Loyola School, Kerala
  • Ms. Shelly Batra, TGT, Mount Carmel School, Delhi
  • Ms. Smitha R Athreya, PGT, Delhi Public School Rourkela
  • Ms. Soumya Iyer, PGT, Sanskriti School, Pune
  • Ms. Swati Sharma, TGT, Heritage Xperiential Learning School, Gurugram
  • Mr. Tushar Upadhyay, TGT, Navrachana Higher Secondary School, Vadodara
  • Ms. Varsha Vijay K, TGT, Delhi Public School Bangalore North
  • Ms. Vineeta Garg, PGT, Shaheed Rajpal DAV Public School, Delhi

Foreword

The transformation of technology through Artificial Intelligence (AI) is reshaping industries and society at large. This revised textbook encompasses core AI concepts, applications, and ethical considerations, emphasizing the relevance of AI in students' future professional landscapes. This curriculum, administered by the Central Board of Secondary Education (CBSE) and developed in collaboration with IBM, provides a robust framework for students in Classes XI and XII. Engaging with this material will not only familiarize students with AI theories and algorithms but also enhance their critical thinking and problem-solving skills in this evolving technological context.

AI Curriculum Structure

Aligns with National Skills Qualification Framework (NSQF) Standards.

INDEX

  • Unit 1: Python Programming-II
  • Unit 2: Data Science Methodology
  • Unit 3: Making Machines See
  • Unit 4: AI with Orange Data Mining Tool
  • Unit 5: Introduction to Big Data and Data Analytics
  • Unit 6: Understanding Neural Networks
  • Unit 7: Generative AI
  • Unit 8: Data Storytelling

UNIT 1: PYTHON PROGRAMMING-II

Title: Python Programming-II

Approach: Hands-on, Team Discussion, Web search, Case studies
Summary:

This chapter offers a detailed review of fundamental Python programming techniques while ensuring engagement through practical experience with popular libraries pertaining to data analysis and machine learning.

Learning Objectives:
  1. Review basic functionalities of the NumPy library, including arrays and functions.
  2. Import and export data between CSV files and Pandas Data Frames effectively.
  3. Implement a Linear Regression algorithm complete with data preparation and model training steps.
Key Concepts:
  • Recap of NumPy library fundamentals.
  • Recap of Pandas library operations.
  • Techniques for Data handling between CSV and Data Frames.
  • Strategies for dealing with missing values.
  • Understanding Linear Regression.
Learning Outcomes:

Students will demonstrate proficiency in:

  • Data manipulation using NumPy and Pandas.
  • Performing data import/export ensuring integrity.
Prerequisites:

Foundational understanding of Python introduced in Class XI alongside fundamental programming experience.

1.1. Python Libraries

Python libraries encapsulate pre-defined code sets that streamline programming tasks, notably in data science and analytics. Two main libraries to focus on are NumPy and Pandas, which are critical for data handling and enhancement in AI applications.

1.1.1 NumPy Library:

NumPy, short for Numerical Python, is pivotal for numerical computing, offering extensive functionalities primarily for array manipulation. The rank of an array indicates its dimensionality.

1.1.2 Pandas Library:

Pandas is vital for data analysis tasks in AI contexts, aiding in importing, analyzing, and visualizing data. For example, utilizing data from marketing campaigns allows for efficient group-wise performance analysis. With Pandas, generating insights via complex visualizations becomes seamless.

1.2. Pandas Data Structures

Pandas largely employs two data structures:

  • Series
  • DataFrame
i) Creating Series and DataFrames:
  • Creating Series from Scalar values
  • DataFrames from NumPy arrays
   import pandas as pd
   import numpy as np
   array1 = np.array([90,100,110,120])
   marksDF = pd.DataFrame([array1], columns=['Maths', 'Science', 'English'])
   print(marksDF)
iii) Creating DataFrame from a Dictionary
   data = {'Name':['Varun', 'Ganesh'], 'Age':[34,40]}
   df = pd.DataFrame(data)
   print(df)
Deleting Rows or Columns:

Achieve row/column deletion with parameters (axis=0 for rows, axis=1 for columns):

drop(rows, axis=0)
drop(columns, axis=1)
1.3. Handling Missing Values

Missing values can be managed principally by dropping or estimating. The isnull() function helps to identify missing data, while the dropna() and fillna() functions handle them respectively.
Activity Case Study: Understanding missing values through student marks sheet analysis demonstrating NaN representation and imputation strategies using the mean of columns.

UNIT 2: Data Science Methodology

Introduction

Students are introduced to the Data Science methodology as detailed by John Rollins at IBM. This structured approach visualizes data science and its applications effectively.

Learning Objectives:
  1. Demonstrate understanding of the data science methodology steps.
  2. Recognize the significance of model validation and iteration.

Steps in Data Science Methodology

  1. Problem Definition
  2. Data Collection
  3. Data Processing
  4. Modeling
  5. Evaluation
  6. Deployment
  7. Feedback
Conclusion

Each methodology step sets the foundation to follow sequentially, ensuring comprehensive execution of data science projects effectively.

Ethical Considerations in Data Science

Essential to discuss the impacts and developments to ensure fair and responsible use of AI technologies whilst maintaining data integrity and privacy.

UNIT 6: Understanding Neural Networks

Introduction to Neural Networks

Neural Networks mimic human brain structures, consisting of input, hidden, and output layers, each processing data to generate outputs/decisions.

Key Components of Neural Networks
  • Neurons
  • Weights
  • Activation functions
  • Learning rules
Types of Neural Networks
  • Feedforward Neural Networks (FFNN)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)

Real World Application Cases

Understanding their deployment in practical applications from image recognition to NLP showcasing their versatility and effectiveness in contemporary AI solutions.

Evaluation Techniques for Neural Networks

Use backpropagation and learning rules to optimize the performance continually, demonstrating their relevance in practical ML scenarios.

Future Trends and Societal Impact of Neural Networks

Exploring the implications of wide integration across various sectors driving innovation and addressing potential ethical challenges.

UNIT 7: Generative AI

Introduction

Understand the basis and workings of Generative AI including models like GANs and VAEs. Evaluating their societal impact and different applications across fields like art, data creation, etc.

Applications of Generative AI
  • Creating content in arts and media.
  • Text generation with practical applications in customer service.
  • Ethical implications regarding misinformation and biases.

LLMs

Large Language Models (LLMs) such as GPT and BERT, focusing on capabilities and technological infrastructure that support their efficiency in data generation and manipulation.

UNIT 8: Data Storytelling

Introduction

Understanding the importance of storytelling and how it intertwines with data, emphasizing compelling narrative with rich visuals to enhance comprehension and engagement in data-driven insights.

Key Elements in Data Storytelling
  • Data
  • Narrative
  • Visual Representation
    Utilizing these elements strategically to convey the essence of data effectively enhancing decision-making processes and driving engagement with audiences across various platforms.