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• Fraud and Risk Detection: The Banking and finance sector uses machine learning to analyse customer data, like
                   profiling, past expenditures, and other key variables to assess risks, defaults, and potential failures. This helps
                   them minimise bad debts and losses by offering banking products based on a customer’s purchasing power.
                    • Object Classification: ML model learns to identify and name objects in images and videos. For example, if
                   you show it pictures of animals like cats and dogs, it learns what each looks like. Later, when you give it a new
                   picture, it can tell whether it’s a cat or a dog.
                 DL


                 DL stands for Deep Learning. It is a subset of Machine Learning inspired by the structure and function of neurons
                 in the human brain, leading to the development of Artificial Neural Networks (ANNs). Deep Learning involves
                 implementing  neural  networks  to  process  high-dimensional  data,  extract  meaningful  insights,  and  provide
                 solutions to complex problems.



















                                   Input Layer                 Hidden Layers                Output Layer

                 In the hierarchy of Artificial Intelligence (AI), AI represents the broader field, with Machine Learning (ML) as a
                 subset, and Deep Learning (DL) as a further specialised subset of ML. While ML focuses on algorithms to learn
                 from data, DL represents the next evolution of ML. DL is capable of handling large datasets and complex tasks with
                 multi-layered neural networks.
                 Difference between Deep Learning and Machine Learning is shown below:

                  Parameters                    Machine Learning                               Deep Learning
                  Data          Machine Learning algorithm can easily work  with  When the size of the data is small,
                  Dependency    smaller data set.                                   a Deep Learning algorithm does
                                                                                    not perform well 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.
                  Problem       When we are solving  a problem using a traditional  Deep Learning algorithm solves  the
                  Solving       machine learning algorithm it is generally recommended  problem end to end.
                  Approach      that  we  first  break  down  the  problem  into  different
                                sub parts and solve them individually and then finally
                                combine them to get the desired result.






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