Page 235 - Artificial Intellegence_v2.0_Class_9
P. 235

Till now, you have learnt about problem scoping, data acquisition and data exploration. After data exploration,
            the next step in the AI project cycle is data modelling.


                    What is Modelling?


            Modelling or data modelling is defined as the process of designing decision-making algorithms that has to be
            trained on a set of data (which was acquired at the data acquisition stage for the problem you scoped in the
            problem-scoping stage) and apply that learning to recognise certain types of patterns.
            Once the data is visualized and trends are formed, we need to work with algorithms to prepare the AI model.
            This can be done by designing our own models or using the existing AI models. Before we go into the details of
            modelling, let us first understand the following important terms:

               • Artificial Intelligence: AI refers to any technique that enables computers to mimic or imitate, develop and
              demonstrate human intelligence. They are machines that can perform tasks that they are programmed for. AI
              enables machines to think without any human intervention.
               • Machine Learning: Machines need to learn the ways of humans by learning the techniques and processes. So
              machine learning is a subset of artificial intelligence that uses statistical methods that enable machines to
              improve with experiences. So machines learn from their mistakes and take them into consideration in the next
              iteration,  this  way  they  keep  improving  with  experience.  For  example,  Snapchat  filters  and  Netflix
              recommendations.
               • Deep Learning: Machines can draw  meaningful inferences from large               Artificial
              volumes of datasets. In deep learning, the machine is trained with a huge         Intelligence
              amount of data  which helps it train itself. Deep learning is  a machine            Machine
              learning algorithm that is inspired by the functionality of our brain cells         Learning
              called neurons. For example, Google translate, image recognition in social
              media apps.                                                                          Deep

            Artificial intelligence is an umbrella term that holds machine learning and           Learning
            deep learning. Deep learning follows a specific learning approach which is a
            subset of machine learning comprising multiple machine learning algorithms.


                    Difference between AI, Machine Learning and Deep Learning

            Let us understand the difference between artificial intelligence, machine learning and deep learning.


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

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





                                                                                        AI Project Cycle   233
   230   231   232   233   234   235   236   237   238   239   240