Class 12: Capstone project | Expected Questions | CBSE 2024 | AI 843 |Artificial Intelligence|Aakash

The chapter introduces the concept of creating projects, emphasizing the importance of theory in practical project development.

Discusses the evaluation process to determine the usefulness and success of a project, highlighting the need for analysis and evaluation.

Explains the process of problem-solving, data analysis, and creating effective solutions for various issues through data exploration and graphing.

Covers the process of creating projects independently, including conducting research, coding, and presenting the projects to gain a deeper understanding of project creation.

Introduces the concept of design thinking, simplifying problems, creating effective solutions, and applying analytical approaches to problem-solving.

Explains voice recognition techniques, data conversion into sound, and the application of voice recognition technology in various scenarios for problem-solving.

Discusses the analytical approach in data science, predicting outcomes, using reinforcement learning techniques, and harvesting crops using data science principles.

Explains problem-solving techniques, the importance of choosing the right approach, and determining the data formats and types required for different solutions.

Creating a model in classification algorithms, including unsupervised algorithms. Evaluating model quality using various parameters and data split methods for better solutions.

Starting something new and the need for promotion to the next level. Evaluating and validating the model with techniques like non-cross-validation and understanding testing data.

Explanation of testing data, its importance in problem-solving, and techniques like data split and classification based on examples.

Discussing techniques like non-cross-validation and folding in model quality evaluation. Utilizing different types of techniques to validate and improve model quality.

Understanding regression techniques and their applications in predicting numbers accurately. Discussing distance error calculations and techniques for improving model accuracy.

The importance of pattern recognition in problem-solving and how it helps in understanding and resolving complex problems. Discussing different types of data sets and their patterns.

Analyzing data and creating multiple solutions to a problem. Understanding different types of data sets and creating various solutions.

Finalizing problem-solving by dividing large problems into smaller ones. Discussing the process of code creation and tackling big problems effectively.

Evaluating models and dividing complex problems into smaller, manageable ones. Strategies for coding, creating solutions for big problems, and handling small problems effectively.

Differentiating between training and testing data and the significance of effective composition and testing in machine learning.

Importance of asking yourself questions regarding data analysis and preparing different types of data sets related to finance, marketing, advertisement, and decision-making.

Exploring design thinking and its role in problem-solving. Understanding problems, ideation, ongoing problem-solving, and creating easy solutions for complex problems.

Dividing complex problems into smaller pieces, coding and creating solutions, and tackling big problems through division and coding.

Strategies for handling different types of problems, dividing them into smaller parts, and effectively coding and solving complex issues.

Explains the process of configuring the train test split using multiple weights for training and testing data. Discusses the importance of test size and training size in the split.

Discusses the use of cross-validation for model update, including non-hold cross-validation. Talks about the time taken for running cross-validation and the repeated training and testing process for small and large data sets.

Discusses how to decide the train-test split parameters for neural networks, including the number of hidden layers and classes in clustering. Mentions quickly calculating squared errors and considering the analytical approach for questions in exams.

Focuses on understanding business problems, project sizes, analytics techniques, data cleaning, model feeding, and the accuracy of models. Explains the concept of IT performance deployment and real-time IT usage.

Addresses the importance of checking data quality, distinguishing between fake and original data, extracting anti-values, cleaning and improving data for model feeding, accurate modeling with good data, and deploying models for IT performance.