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Artificial intelligence is an umbrella term that includes machine learning and deep learning. Deep learning follows a
              specific learning approach,  which is a subset of machine learning comprising multiple machine learning algorithms.
              Let us understand the difference between artificial intelligence, machine learning and deep learning.


                   Artificial Intelligence          Machine Learning                       Deep Learning
               It aims at making a machine   It aims at making a machine that   It aims at building neural network that can
               that mimics human             can learn through data and solve   help in discovering patterns or trends.
               intelligence.                 complex problems.
               It is a subset of data science.  It is a subset of AI.         It is a subset of machine learning.

               It is the simulation of       It is the training of machines to   It is the process of using artificial neural
               intelligence in machines.     take decisions with experience.  networks for solving complex problems.

               Examples: Robotics, natural   Examples: Decision trees, random  Examples: Convolutional neural networks
               language processing,          forests, support vector machines,   (CNNs), recurrent neural networks (RNNs),
               computer vision, expert       neural networks, etc.            generative adversarial networks (GANs),
               systems, etc.                                                  etc.


              Data Modelling Techniques

              In AI modelling, we develop different algorithms called models, which can be trained to produce intelligent output.
              In other words, we write code to make a machine artificially intelligent.
              AI modelling techniques can be broadly classified into two approaches, namely, rule-based and learning-based.
              Let us learn about them in detail.


              Rule-Based Approach

              Rule-based approach is based on a set of rules and set of facts already fed to the machine to generate the desired
              output. These models can operate with simple basic information and data. The relationships or patterns in the data
              is defined by the developer. To explain it further, let's take an example. You have a dataset comprising 100 images
              of cars and 100 images of cycles. To train your machine, you feed this data and label each image as either a car or
              a cycle. Now if you test the machine with an image of a car, it will compare with the trained data and according to
              the labels of the trained data it will identify it as a car. This is called a rule-based approach. The rules given to the
              machine in this example are the labels assigned to the training data.



                                    Labelled   Rule-based   Training
                                    Datasets    Approach      Data                       Output
                                   Used to Train
                                     Machine                 Used for        Testing             Machine Identifies the
                                                  Model       Testing         Data                   Image as Car




              Learning-Based Approach

              Learning-based approach refers to a model where the relationships or patterns in the data are not explicitly defined
              by the developer. In this approach, random data is fed into the machine and the machine develops its pattern or
              trends based on the data outputs. It is an alternative method to address some of the challenges of rule-based
              systems. This approach is typically followed when the dataset fed to the machine is unlabelled and too random.


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