Machine Learning and Deep learning

                                   How Useful is Self-Supervised Learning? Self-supervised learning is a way of  training computers to do tasks without humans providing labelled data. It is a main subset of unsupervised learning where outputs or are derived by machines that label, categorize, and analyze information on their own then draws conclusions based on correlations and connections. Self-supervised learning can also be an autonomous form of supervised learning because it does not require human input in the form of data labelling. In contrast to unsupervised learning, self-supervised learning does not focus on clustering and grouping that is commonly associated with unsupervised learning.  The concept of self-supervised learning aims to address challenges in supervised learning when it comes to collecting, handling, cleaning, labeling, and analyzing data. Developers who want to create an image classification algorithm , therefore, create supervised learning-capable systems to co

                    What is the Role of Predictive Analytics in Present world? Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes   using historical data combined with statistical modeling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities. Predictive analytics is often associated with big data and data science. Companies today are swimming in data that resides across transactional databases, equipment log files, images, video, sensors or other data sources. To gain insights from this data, data scientists use deep learning and machine learning algorithms to find patterns and make predictions about future events. These include linear and nonlinear regression, neural networks, support vector machines and decision trees. Learning obtained through predictive analytics can then be used further within prescriptive analytics to drive actions