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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.



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