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What is ML?
              Machine Learning (ML) is a subset of Artificial
              Intelligence (AI) that  focuses on building
              systems  that learn from data,  identify
              patterns, and make decisions  without
              explicit programming. It enables computers
              to improve their performance over time by
              analysing large sets of data and  using that
              information to make predictions or decisions.

              Types of Machine Learning
              ML is applied in various industries and can be categorised into three main types:

              1. Supervised Learning: This type of ML uses labelled data to train the model. The algorithm learns from
                 the input-output pairs, and the goal is to make predictions based on new, unseen data. For example, spam
                 email detection and image classification, such as identifying objects in photos.
              2. Unsupervised Learning: In this type, the algorithm is trained on data that is not labelled, and it tries to
                 find patterns or groupings in the data.
                Applications include customer segmentation and anomaly detection, where the system identifies unusual
                 behaviour in a dataset.

              3. Reinforcement Learning: This type of ML involves an agent that learns by interacting with an environment
                 and receiving feedback in the form of rewards or penalties. It is used in applications like game-playing AI
                 (e.g., AlphaGo) and robotics, where machines learn to complete tasks through trial and error.

              Applications of Machine Learning
              Machine Learning is  being increasingly applied across  various industries to enhance decision-making,
              automate processes, and optimise performance. Here are some key applications of ML:
                Healthcare: ML is used to analyse medical data, diagnose diseases, and predict patient outcomes. For
                 example, ML algorithms are helping radiologists detect diseases like cancer in medical imaging, and tools
                 like IBM Watson assist in personalised treatment recommendations.

                Finance: In the finance industry, ML algorithms are used for fraud detection, credit scoring, and algorithmic
                 trading. For instance, credit card companies use ML to identify suspicious transactions, while hedge funds
                 use it for high-frequency trading strategies.
                E-commerce and Retail: ML is applied to recommend products based on consumer preferences and past
                 purchases. E-commerce platforms like Amazon use ML to suggest products to customers, driving sales and
                 enhancing user experience.

                Autonomous Vehicles: ML enables self-driving cars to analyse data from sensors and cameras, allowing
                 them to navigate and make real-time decisions. Companies like Tesla and Waymo use ML to improve their
                 autonomous driving technology.
                Social Media and Content Recommendation: Platforms like Facebook, Instagram, and YouTube use ML
                 to personalise content and recommend posts, videos and advertisements based on user behaviour and
                 interactions.
                Customer Service: ML-powered chatbots and virtual assistants help businesses automate customer service
                 tasks, providing immediate responses to queries, resolving issues, and improving customer experience.

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