ROHIT's Smart Class Room

  Semi-Supervised Learning: - Semi-supervised machine learning is a learning approach that uses a small amount of labelled data and a large amount of unlabelled data to train a model. semi-supervised learning is needed for                    1.  Labelling data is time-consuming and expensive                    2.  Unlabelled data is easy to collect                    3.  Semi-supervised learning improves accuracy compared to using only a small labelled dataset    Semi-supervised learning works     1.  Start with a small labelled dataset     2.  Train an initial model     3.  Use the model to predict labels for unlabelled data     4.  Select confident predictions     5.  Retrain the model using both l...

  Unsupervised Learning: Unsupervised learning is a type of machine learning where the algorithm or model is trained and learns to recognize or finds patterns on data without labelled data examples. There is no “right answer” given to the system. Instead, the algorithm explores the data and discovers hidden patterns, structures, or relationships on its own. In unsupervised learning, the dataset does not have labels or target values. The algorithm must learn to identify inherent patterns without guidance on what the “correct” answers are. The main aim of the unsupervised learning algorithm is to group or categories the unsorted dataset according to the similarities, patterns, and differences. Machines are instructed to find the hidden patterns from the input dataset.   unsupervised learning works                         *  Input data is unlabeled          ...

  Supervised Learning in Machine Learning Supervised learning is a machine learning approach where a model is trained using labeled data .  Each training example includes input data and the correct output.  The model learns the relationship between inputs and outputs and then uses that learning to make predictions on new, unseen data. In simple terms, the system learns under guidance, similar to how a student learns from a teacher who provides correct answers. Supervised Learning Works Collect labeled data (input + correct output) Split data into training and testing sets Train the model using the training data Test the model on unseen data Use the model for real-time predictions Real-Time Examples of Supervised Learning 1. Email Spam Detection (Classification) Input : Email content, sender, keywords Output : Spam or Not Spam Real-time use : Gmail filters spam emails automatically based on past labeled emails. 2. Student Performance Prediction ...