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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.

              C.  State whether these statements are true or false.
                  1.  Machine learning can be broadly classified into Supervised, Unsupervised, and Reinforcement learning.   ……….……

                  2.  The best clustering is the one that minimizes the repetitions.                           ……….……
                  3.  Unsupervised learning approach works on a labelled dataset.                              ……….……

                  4.  Features in a dataset are represented by columns in a table.                             ……….……

                  5.  Hidden layer is visible to the outside layer.                                            ……….……
                  6.  AI can fully replicate the complexity of the human brain.                                ……….……

                  7.  ML models can predict outcomes without explicit programming.                             ……….……
                  8.  Artificial Neural Networks (ANNs) are inspired by the structure and function of neurons
                    in the human brain.                                                                        ……….……

              D.  Match the following:

                  1.  Anomaly Detection           a. Unsupervised Learning
                  2.  Generative AI               b. Supervised Learning
                  3.  Learning-based Rule         c. Discrete dataset
                  4.  Dimensionality Reduction     d. Medical diagnostics
                  5.  Clustering                  e. Deep 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.  What do you understand by Digit Recognition in context of Deep Learning?
                Ans.  In this process, models are trained to identify handwritten digits. The model learns patterns from a large dataset of
                    images. This process demonstrates how Deep Learning can automate tasks requiring pattern recognition.
                  4.  What do you mean by a testing data set?
                Ans.  The testing data set is a collection of data provided to a machine learning model to evaluate how well it has learned to
                    make predictions.
                  5.  Name the two types of Deep learning models.

                Ans.  There are two types of Deep Learning Models: Artificial Neural Networks (ANN) and Convolutional Neural Network (CNN).
                  6.  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.


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