Lesson 5: Solving problems with ML models

What will you work through to create a classification model?

• AI project lifecycle

• Data collection process

• Model evaluation techniques

• Feature selection methods.

What is the first stage in the AI project lifecycle?

• Data Collection

• Defining the problem

• Model Training

• Evaluation

Why is creating an ML model considered a suitable approach?

• It is cost-effective

• It is a traditional method

• It is a suitable approach

• It is a new technology.

Fill in the blank: The age of 'Drona' is ____ years.

• 9

• 11

• 4

• 32

Match the following steps with their descriptions in preparing data for a machine learning model.

• Removing errors and inconsistencies from the dataset.

• Cleaning the data

• Gathering additional information to improve model accuracy.

• Using the data without changes.

• Applying the dataset as it is without any modifications.

• Ignoring the data

• Disregarding the dataset entirely.

• Collecting more data

The primary goal of data cleaning in machine learning is to (a)  

• To reduce data size
• To make data more complex
• To remove errors and inconsistencie
• To increase data volume

(a)   is a common method for evaluating a machine learning model.

• Feature extraction
• Cross-validation
• Data collection
• Data cleaning

Match the purpose of feature selection in machine learning with the correct description.

• To add more variables to the model

• To select the most relevant features

• To disregard every variable in the dataset

• To ignore all features

• To pick variables without any specific criteria

• To randomly choose features

• To identify and use the most important variables

• To increase the number of features