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CBSE Class 12th AI (Artificial Intelligence)
Explore and master the fundamentals of CBSE Class 12th AI (Artificial Intelligence)
You've not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.Chapter 1
Communication Skills – IV
This chapter emphasizes the importance of advanced communication skills, including verbal, non-verbal, and written communication, in professional settings. It highlights the communication cycle and common barriers to effective communication while providing techniques to overcome these barriers. Skills in email etiquette, report writing, and workplace conduct are crucial for success in artificial intelligence and beyond.
Chapter 2
Self-Management Skills – IV
The chapter explores advanced self-management skills that are crucial for personal and professional success, particularly in AI careers. Key areas covered include managing stress, developing emotional intelligence, motivation, goal setting, time management, adaptability, resilience, and interpersonal skills. These competencies are emphasized as essential for effective decision-making and lifelong learning in a tech-driven environment.
Chapter 3
Information and Communication
Advanced ICT skills are essential in today's digital landscape, focusing on word processing, spreadsheet manipulation, presentation tools, and digital citizenship. Learning to use these technologies efficiently enhances personal, academic, and professional communication while fostering responsible online behavior. The chapter also emphasizes collaboration through online tools and highlights the importance of cybersecurity in the digital age.
Chapter 4
Entrepreneurial Skills – IV
Entrepreneurship serves as a critical catalyst for innovation, job creation, and economic development. This chapter delves into various aspects of entrepreneurship, including the significance of business planning, marketing strategies, risk management, and funding options. Additionally, it emphasizes the importance of sustainable development in business practices and showcases inspiring success stories of young entrepreneurs in India.
Chapter 5
Green Skills – IV
The chapter provides a comprehensive overview of sustainable development and green skills, emphasizing the critical role individuals, especially youth, play in promoting a greener future. It details the significance of the green economy, the integration of artificial intelligence in sustainable practices, and highlights various green job opportunities. Through practical applications and responsible innovation, the chapter aims to empower students to actively engage in environmental preservation efforts.
Chapter 6
Introduction to Artificial Intelligence
Artificial Intelligence (AI) is reshaping the modern world by enabling machines to perform tasks that traditionally required human intelligence. The chapter discusses the definition, types, history, domains, applications, benefits and limitations of AI, as well as ethical implications. Understanding these concepts is essential for leveraging AI's potential responsibly and ethically.
Chapter 7
AI Project Cycle
The AI Project Cycle outlines a structured approach to developing and deploying AI solutions. It encompasses defining the problem, acquiring and analyzing data, training models, evaluating their performance, and deploying them effectively. Each phase is crucial in ensuring that the AI project meets its objectives while adhering to ethical standards and ensuring user satisfaction.
Chapter 8
Advanced Python – Revision and Functions
The chapter revisits core Python concepts and focuses on advanced function-related topics essential for clean and reusable code. Functions are introduced, covering their types, parameters, scopes, and applications, including recursion and lambda functions. By understanding these elements, one can effectively structure Python code for various applications, particularly in AI development.
Chapter 9
Data Analysis using Python
Data analysis is fundamental to extracting meaningful insights from raw data, and this chapter introduces techniques using Python libraries such as Pandas, NumPy, and Matplotlib. Key skills include data loading, cleaning, manipulation, and visualization, all of which form the basis for more advanced applications in Machine Learning and Artificial Intelligence. The chapter also covers hands-on project work to reinforce the concepts learned.
Chapter 10
Introduction to Neural Networks
Neural networks are computational models that simulate the human brain to process information, forming the foundation of deep learning in AI. They consist of interconnected layers of neurons, which learn from large datasets to perform tasks such as image recognition and language processing. While highly effective, neural networks have limitations, including requiring substantial computational resources and large amounts of data.
Chapter 11
Natural Language Processing (NLP)
Natural Language Processing (NLP) is a crucial branch of Artificial Intelligence that facilitates machine comprehension and generation of human languages. It incorporates elements from linguistics, computer science, and machine learning to enhance applications such as chatbots, translation, and sentiment analysis. Despite its advantages, NLP faces challenges such as ambiguity and the ethical implications of biases in data. Advances in technology continue to enhance the sophistication of NLP in everyday applications.
Chapter 12
Evaluation Methodologies of AI Models
Evaluating AI models is crucial for understanding their performance in real-world scenarios, including checking predictions, error rates, and ensuring fairness. Various methodologies such as confusion matrices, evaluation metrics, cross-validation, and ROC curves provide frameworks to assess model quality. These techniques not only help in selecting the best-performing models but also address issues of bias and fairness in AI applications.