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B. Fill in the blanks.
1. The ……….……................ approach refers to a model where the relationship or patterns in the data are not defined by the
developer.
2. Machine Learning models improve their performance using ……….……................ data.
3. ……….……................ is an unsupervised learning technique used to group similar data points into clusters.
4. ……….……................ is a mathematical approach to find a relationship between two or more variables.
5. A Neural Network is divided into multiple layers and each layer is further divided into several blocks called
……….…….................
6. Neural networks are primarily used for solving problems with ……….……................ datasets, like images.
7. The Speech Recognition devices use ……….……................ to understand spoken language and convert speech to text.
8. ……….……................ Learning is the next evolution of machine learning.
SECTION B (Subjective Type Questions)
A. Short answer type questions.
1. Define Deep Learning.
Ans. Deep Learning is a subset of Machine Learning that uses neural networks to process large amounts of data and solve
complex problems.
2. Write two examples of Machine Learning.
Ans. Recommendation Systems and Spam Email Filtering
3. Define modelling.
Ans. AI Modelling refers to developing algorithms, also called models which can be trained to get intelligent outputs. That
is, writing codes to make a machine artificially intelligent. The model is trained using data.
4. What is a Training Dataset?
Ans. A collection of data provided to a machine learning model to help it analyse and learn patterns is called training data.
5. Name two types of learning-based approaches.
Ans. The two types of learning based approaches are: Machine Learning and Deep Learning.
B. Long answer type questions.
1. Differentiate between Machine learning and Deep learning.
Ans. The difference between ML and DL are as follows:
Parameters Machine Learning Deep Learning
Machine Learning algorithm can easily work with When the size of the data is small, a Deep
Data smaller data set. Learning algorithm does not perform well
Dependency as a deep learning algorithm needs large
amounts of data to understand perfectly.
Hardware Machine Learning algorithms can work on low end Deep Learning algorithms are heavily
Dependency machines as well. dependent on high-end machines.
When we are solving a problem using a traditional Deep Learning algorithm solves the
Problem machine learning algorithm it is generally problem end to end.
Solving recommended that we first break down the problem
Approach into different sub parts and solve them individually and
then finally combine them to get the desired result.
Machine Learning algorithms take much less time to Usually, Deep Learning algorithms take a
Execution train. long time to train because there are many
Time parameters making the training time longer
than usual.
Advanced Concepts of Modeling in AI 135

