SUPERVISED LEARNING AND UNSUPERVISED LEARNING

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1. SUPERVISED LEARNING AND UNSUPERVISED LEARNING 212406006 Baginda Syahran

2. Supervised Learning 01

3. What is supervised learning? Supervised learning, also known as supervised machine learning, is a subcategory of machine learning and artificial intelligence. It is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately. As input data is fed into the model, it adjusts its weights until the model has been fitted appropriately, which occurs as part of the cross validation process.

4. How supervised learning works Supervised learning can be separated into two types of problems when data mining—classification and regression: Classification utilizes an algorithm to accurately assign test data into specific categories. It identifies distinct entities within the dataset and aims to draw conclusions on how those entities should be labeled or defined. Common classification algorithms include linear classifiers, support vector machines (SVM), decision trees, k-nearest neighbor, and random forest, which are described in more detail below. Regression is employed to understand the relationship between dependent and independent variables. It is commonly used for making projections, such as forecasting sales revenue for a given business. Popular regression algorithms include linear regression, logistic regression, and polynomial regression.

5. Supervised learning algorithms Neural networks: Neural networks are mainly utilized in deep learning algorithms. They process training data by simulating the interconnectedness of the human brain through layers of nodes. Naive Bayes: Naive Bayes is a classification approach that follows the principle of class conditional independence derived from Bayes' Theorem. It assumes that the presence of one feature does not affect the presence of another feature when determining the probability of a given outcome. Each predictor has an equal impact on the final result. Linear regression: Linear regression is employed to determine the relationship between a dependent variable and one or more independent variables. It is commonly used to make predictions about future outcomes.

6. Unsupervised Learning 02

7. What is unsupervised learning? Unsupervised learning uses machine learning algorithms to analyze and cluster unlabeled datasets. It discovers hidden patterns or groupings in the data without human intervention. This makes it ideal for tasks like exploratory data analysis, customer segmentation, cross-selling strategies, and image recognition

8. Common unsupervised learning approaches Clustering Clustering is a data mining technique that groups unlabeled data based on similarities or differences. It organizes data into clusters, representing patterns in the information. There are various types of clustering algorithms, including exclusive, overlapping, hierarchical, and probabilistic. Ex Exclusive and Overlapping Clustering Exclusive clustering, also known as "hard" clustering, assigns each data point to only one cluster. The K-means algorithm is an example of exclusive clustering.

9. Unsupervised learning: No predefined labels. Algorithm analyzes and clusters unlabeled data to discover patterns or structures. Used for exploratory analysis, clustering, and anomaly detection. Supervised learning: Predefined labels available. Algorithm learns from labeled data to make predictions or classify new data. Used for regression (predicting continuous values) and classification (assigning labels). Unsupervised vs. supervised

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