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Machine learning algorithms can learn from different kinds of information, such as pictures, text, sensor readings,
              and past data, by figuring out patterns in the data to guess or decide things. Additionally, some common machine
              learning methods such as decision trees, neural networks, and support vector machines enable this learning process.

              Features of Machine Learning
              Some key features of machine learning are as follows:

              ●  ML interprets, analyses, and processes data to address real-world problems.
              ●  It learns from data and enhances its performance over time.

              ●  The technology facilitates automation and prediction based on learned patterns.

              ●  It is the prevailing approach in contemporary AI.
              ●  It employs data analysis, training, and sometimes human review to refine its capabilities.
              ●  Unlike traditional programming, it doesn’t rely on predefined rules but learns from examples and experiences.

              ●   It powers a wide range of applications across industries, from healthcare and finance to autonomous vehicles and
                  recommendation systems.
              However, ML is not without its challenges. Overfitting, in which models specialise too much on training data, can
              result in poor performance on fresh data. Bias in training data can lead to skewed predictions, and some models
              are difficult to understand, serving as black boxes. Despite these hurdles, machine learning (ML) converts data into
              knowledge, allowing computers to learn, adapt, and make autonomous judgments.


                       Types of Machine Learning


              Machine learning can be divided into three primary categories, each distinguished by its learning approach and nature of
              the input data:
                                                               • Labelled data
                                                               • Direct feedback
                                                               • Predict outcome

                                          Supervised
                                            Learning


                                                                                           • No labels
                          Machine                            Unsupervised                  • No feedback
                          Learning                             Learning                    • Find hidden structure




                                         Reinforcement
                                            Learning

                                                              • Decision process
                                                              • Reward system
                                                              • Learn series of actions
              Supervised Learning
              Supervised learning is a type of machine learning in which machines are trained using well "labelled" training data,
              and on basis of that data, machines predict the output. The labelled data means some input data is already tagged
              with the correct output.

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