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    Semi-Supervised Learning
                  Let’s go back to our classroom example. Your teacher gives you a mix of solved and unsolved problems. There are only a
                  few solved ones, but a huge number of unsolved ones. You first study the solved examples to get a basic understanding.
                  Then,  you  use  that  knowledge to  try and solve the  other  problems.  As you  work through them,  you  gain  a deeper
                  understanding of the entire topic.
                  Self-supervised learning operates on this principle. It uses a small amount of labelled data to get a head start, and then
                  uses a much larger amount of unlabelled data to complete the learning process. This is particularly useful when getting
                  labelled data is difficult or expensive, like in many real-world scenarios.

                      Example: Consider a medical imaging system designed to detect a rare disease. Getting a doctor to manually label
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                      thousands of X-ray images for the disease is extremely time-consuming and costly. Instead, you can use a small
                      set of labelled images (those already diagnosed by a doctor) to train the model. The model then uses this initial
                      knowledge to analyse a huge dataset of unlabelled X-rays, using patterns it learned to identify potential cases on its
                      own, which can then be verified by a doctor.
                  Reinforcement Learning
                  Think about learning to ride a bicycle. You don’t have a textbook with step-by-step instructions. Instead, you learn by
                  doing. You pedal a little, and if you stay balanced, you feel a sense of achievement (a reward). If you wobble and fall, it’s
                  a negative experience (a penalty). Over time, you learn which actions (like turning the handlebars or leaning your body)
                  lead to rewards and avoid the actions that lead to penalties, until you can ride perfectly.
                  This is the core of Reinforcement Learning. An agent (the machine) learns to make decisions by interacting with an
                  environment. It receives rewards for ‘good’ actions and penalties for ‘bad’ actions. Its ultimate goal is to learn a strategy,
                  or a policy, that maximises its total reward over time.

                      Example: Have you ever seen a robotic arm in a factory? Using reinforcement learning, you can train a robotic arm
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                      to pick up an object and place it in a box. You don’t program every single movement. Instead, you give it a reward
                      when it successfully places the object and a penalty if it drops it. The robot learns through a process of trial and error,
                      adjusting its movements and grip until it masters the task with a high success rate.
                  Self-Supervised Learning                                    How Does Machine Learning Work?

                  Finally, let’s  consider a new way of learning. Imagine
                  you’re  given a book  with half  the words missing  from
                  each sentence. You’re  asked to  fill  in the blanks. By
                  understanding the context of the words around the blank   Training
                  space, you can figure out what the missing word should     data
                  be. You’re essentially creating your own learning task from      (Unacceptable)     (Acceptable)
                  the data itself.
                  This is the essence of Self-supervised learning. The model
                  generates  its  own  labels  or ‘tasks’  from the  unlabelled   Train ML     Accuracy           Successful
                  input  data. It learns  by solving these created  tasks,   algorithm                             model
                  which helps it  understand  the underlying  structure  and
                  representations of the data. This pre-training makes the
                  model  very powerful  and ready for  other, more specific
                  tasks later on.                                         Model input
                      Example: A great  example is                           data
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                      training  a  model  to  understand  a
                      language. The model is given vast
                      amounts of text from the internet.
                                                       New input          ML algorithm          Prediction
                                                          data
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                  Touchpad Robotics - XI

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