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  "body_markdown": "2. Types of Machine Learning & their basic workflow\nUpdated\nApril 8, 2025\n•\n4\nmin read\n•\nView as Markdown\nM\nMuhammad Fahad Bashir\nMachine Learning, Artificial Intelligence\nIn previous\narticle\nof our ML journey , we explored what machine learning is and how it’s deeply integrated into our everyday lives—from voice assistants to Google Maps to health tech. But now it’s time to go a level deeper.\nIn this article, we will talk about The\ntypes of Machine Learning\nand what a\nbasic ML workflow\nlooks like.\nTypes of Machine Learning\nSupervised Learning — \"Learning with Labels\"\nImagine you're teaching a child to recognize fruits. You show them apple and say “this is a apple.” That’s\nsupervised learning\n—we give the machine both the\ninput\n(features) and the\ncorrect output\n(label) during training. So machine learn pattern from examples and base on that it predicts in the future.\nTypes of supervised learning\nThis is based on the types of final output/label.\nClassification (Predicting a label or category )\nBinary Classification\n:\nTwo outcomes (e.g., spam or not spam)\nMulticlass Classification\n:\nMore than two outcomes (e.g., classifying types of fruits)\nRegression (Predicting continuous values)\nExample\n: Predicting house prices based on size and location\nCommon Supervised Learning Algorithm\nFew are the common algorithms that are used.\nLogistic Regression\n– Used for binary classification problems\nDecision Trees\n– Splits data into decision nodes for easy predictions\nRandom Forest\n– An ensemble of decision trees for better accuracy\nSupport Vector Machines (SVM)\n– Finds the best boundary between classes\nK-Nearest Neighbors (KNN)\n– Classifies based on closest data points\n2. Unsupervised Learning — \"Learning Without labels\"\nHere, the model works on\nunlabeled data\n—no answers are given. It tries to find hidden patterns or structures all by itself.Imagine giving a bunch of photos of apples to a computer and asking it to group similar ones—without telling it what each photo is. That’s\nunsupervised learning\n. It\nfinds hidden patterns or structures\nin data.\nThree Main Categories:\n1. Clustering:\nprocess of\ngrouping similar data points together\nbased on their characteristics, without using labeled outcomes.\nExamples\nCustomer segmentation\n: Grouping customers by buying habits\nDocument clustering\n: Grouping news articles by topic\nSocial media analysis\n: Finding communities within a network\n2. Dimensionality Reduction:\nDimensionality reduction reduces the number of input variables while preserving essential patterns.\nIt’s like compressing a high-res image without losing much quality—making it easier to store, analyze, and visualize.\nExamples\nImage compression\nGene expression analysis\nin biology\nFeature reduction\nbefore training deep learning model\n3. Anomaly Detection\n( outlier detection) is about finding data points that don’t conform to the expected pattern—like catching a glitch in the matrix.\nThink of a bank transaction that suddenly spikes at 3 AM in a country you’ve never been to. Your bank’s machine learning system probably flagged that as\nsuspicious\n.\nExamples\nCredit card fraud detection\nServer/network intrusion detection\nCommon Algorithms:\nK-Means Clustering\n– Groups data into\nk\nclusters\nHierarchical Clustering\n– Builds a tree of clusters\nPrincipal Component Analysis (PCA)\n– Reduces dimensions for better visualization\nt-SNE\n– Visualizes high-dimensional data in 2D/3D\nReinforcement Learning\nIn reinforcement learning, an agent learns by\ninteracting with its environment\nand receiving rewards or penalties based on its actions.\nIt’s like teaching a dog: reward for a trick well done, correction when it doesn’t obey.\nCommon Algorithms:\nQ-Learning\n– Learns the best action to take in a given state\nDeep Q-Networks (DQN)\n– Combines Q-learning with neural networks\nSARSA\n– Similar to Q-learning but learns using the actual action taken\nBasic Workflow\nRegardless of the type, most ML projects follow a\ncommon workflow\n:\nProblem Definition:\nWhat do you want to solve or predict?\nData Collection:\nGather the necessary data\nData Preprocessing:\nClean, format, and split the data\nModel Selection:\nChoose the right algorithm\nTraining the Model:\nTeach it using training data\nEvaluation:\nTest with unseen data to check performance\nTuning:\nImprove accuracy with hyperparameter tuning\nDeployment:\nUse the model in a real-world application\nConclusion\nUnderstanding the\ntypes of machine learning\nis like learning how different superheroes use their powers. Each type has its unique way of learning and solving problems. So whether you're building a spam filter, a chatbot, or training an AI to play chess, knowing which ML type to use is your first step toward success\n#\nmachine-learning\n#\ntypes\n#\ntutorial\n#\ntechnology\n41\nviews",
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