Page 140 - Artificial Intellegence_v2.0_Class_12
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At a Glance
• A capstone pro ect is a comprehensive, independent, and final pro ect undertaken as a part of a curriculum
designed to assess the skills, kno ledge, and e pertise a student has acquired.
• A successful problem defining process requires a basic analysis and evaluation of the pro ect related
problems, their reasons, and methods.
• Design hinking methodology provides a solution based approach to solving problems.
• During coding, e follo problem decomposition methodology that can be applied to real life problems as
ell.
• Once the business problem is clearly stated, the data scientist can define an analytical approach to solving
the problem.
• he analytical approach chosen characterises the requirements for the data.
• During the initial data collection phase, data scientists identify available data sources structured, unstructured,
and semi structured) relevant to the problem area.
• he modelling stage, that begins ith the initial version of the prepared data set, focuses on constructing
predictive or descriptive models based on the previously stated analytic approach.
• he data scientist revie s the model during development and before deployment to determine its quality
and ensures that it correctly and completely ans ers the business problem.
• he train test procedure measures the performance of machine learning algorithms hen they need to make
predictions on data that ere not used to train the model.
• he training dataset is used to fine tune the machine learning model and train the algorithm.
• est dataset algorithms make predictions using the input elements from the training data.
• ross validation is a resampling technique for evaluating machine learning models on a small sample of
data.
• A loss function determines ho ell a certain algorithm models the data.
• oss function learns to lo er prediction error over time ith the help of some optimisation ob ective
function.
• oss functions can be divided into t o categories regression losses and classification losses.
• is sensitive to outliers.
• he oot ean quare rror ) is a metric for determining ho ell a regression line fits the data points.
• yperparameters are parameters hose values govern the learning process.
Exercise
Solved Questions
SECTION A (Objective Type Questions)
ui
A. Tick ( ) the correct option.
. hich of the follo ing is not a part of Design hinking rocess
a. mpathise b. ympathise
c. rototype d. Define
Touchpad Artificial Intelligence (Ver. 2.0)-XII

