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Machine Learning Process



                                                        Machine Learning





                                    Supervised             Unsupervised          Reinforcement
                                    Task Driven             Data Driven            Learn From
                                (Predict Next Value)      (Identify Clusters)      (Mistakes)






            Machine learning algorithms can be used for a wide range of applications, such as image and speech recognition,
            natural language processing, fraud detection, predictive analytics, and autonomous vehicles. For example, machine
            learning can be used to develop chatbots that can understand and respond to queries in a human language or to
            train self-driving cars to recognize and respond to different driving scenarios. Moreover, machine learning can be
            used to create personalized recommendations for users based on their past behavior. It can predict which products
            or services a user is most likely to be interested in. The success of machine learning models depends on the quality
            and quantity of the data used for training. The more data that is available, the more accurate and robust the model
            is likely to be.


                  Supervised learning: Training a model on labelled data to make predictions or classifications based on new,
                  unlabelled data.
                  Unsupervised learning: Training a model on unlabelled data to identify patterns or relationships in the data.
                  Reinforcement learning: Training a model to make decisions based on feedback received from the environment.



                    Fill in the blanks:
                    ___________  is  a  subdomain  of  artificial  intelligence  (AI)  that  involves  the  use  of  statistical  and
                    specialized algorithms to enable computer systems to learn and improve from experience without
                    being explicitly programmed.


            10.1.2 Natural Language Processing

            Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on enabling machines to
            understand, interpret, and generate human language. NLP involves the use of computational algorithms to analyze
            and manipulate natural language data, such as text, speech, and video.





















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