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  "body_markdown": "The ML Diaries — My Machine Learning Journey\nUpdated\nOctober 10, 2026\n•\n4\nmin read\n•\nView as Markdown\nM\nMuhammad Fahad Bashir\nMachine Learning, Artificial Intelligence\nHey everyone 👋\nWelcome to\nThe ML Diaries\n, a place where I am documenting my learning journey from foundational machine learning concepts to practical implementations with code and visuals.\nEach article here is part of a growing series where I explore\nhow algorithms actually work behind the scenes,\nnot just the “what” but the “why” and “how.”\nI’ve organized the series below for an easy step-by-step flow\nFoundation: Getting Started with Machine Learning\n1.\nAn Introduction to Machine Learning & the Relationship\nA beginner’s introduction to what Machine Learning is, how it connects with AI and Data Science, and why it matters in today’s world.\n2.\nTypes of Machine Learning & Their Basic Workflow\nExplains the three main types — Supervised, Unsupervised, and Reinforcement Learning — along with their general workflows.\n3.\nUnderstanding Data, Attributes & Data Objects in Machine Learning\nLearn what data really means in ML — features, attributes, and how they form the foundation of every model.\n4.\nBefore the Model: Essential Steps of Data Preprocessing in ML\nWalk through the must-do data cleaning, handling missing values, normalization, and preparation steps before model training.\nSupervised Learning Series\nIntroduction to Supervised Learning\nA gentle start into supervised learning — understanding labeled data, workflow, and how machines learn from examples.\nA Simple Guide to Logistic Regression with Concepts, Workflow & Real-World Use Case\nUnderstand Logistic Regression in an intuitive way — how it predicts categories and where it’s used in real-world applications.\nLinear Regression — Explained in the Simplest Possible Way\nUnderstand the logic and the math behind this algorithm -\nDecision Tree Algorithm Explained: Simple Guide with Example and Gradio App\nLearn how Decision Trees make data-driven decisions step by step, with a practical example and interactive Gradio UI.\nUnderstanding Random Forest – The Power of Many Trees\nDive deeper into ensemble learning — how combining multiple decision trees improves accuracy and reduces errors.\nUnderstanding K-Nearest Neighbors (KNN): A Simple Yet Powerful ML Algorithm\nExplore how KNN predicts outcomes by learning from nearby data points, with easy examples and explanations of distance metrics.\nExploring the Naive Bayes Classifier: A Simple but Powerful Algorithm\nDive into how Naive Bayes uses probability and prior knowledge to classify data from theory to real-world applications in an easy-to-understand way.\nSVM Made Easy: How Support Vector Machines Work for Classification and Regression\nDiscover how Support Vector Machines (SVM) tackle classification and regression tasks, maximizing confidence and accuracy in machine learning\nUnsupervised Learning Series\nAn Introductory Overview of Un\nsupervised Learning\nDiscover how machines find patterns and group data without labels — covering clustering, association rules, and dimensionality reduction.\nSimplifying K-Means Clustering: A Beginner's Guide to Unsupervised Learning\nwill explore one of the most popular and intuitive algorithms used in unsupervised learning — K-Means Clustering.\nDBSCAN Explained Simply: Clustering with Noise and Arbitrary Shapes\nwill be covering another important clustering algorithm that comes right after K-Means in popularity and usefulness — DBSCAN.\nPCA Explained Simply: Understanding Dimensionality Reduction\nwill be covering\nunsupervised learning technique\nthat reduces the number of features in a dataset while preserving as much important information as possible\nApriori Algorithm — Learning Association Rules the Simple Way\nwill be learning about an algorithm. At first, it sounded complex, but once I broke it down with real-life examples, it started to make sense\nReinforcement Learning\nReinforcement Learning: The Big Pictureinforcement Learning: The Big Picture\nNo labels, no predefined groups, and no clear instructions, and only a goal to achieve?\nQ-Learning: How an Agent Learns From Rewards — Intuition First, No Math\nlearning\nHow does an agent actually learn which action is good?\nQ-Learning in Practice: Understanding the Update Equation with One Simple Example\n- Learning\nHow does the agent actually update the values stored in its Q-table?\nConnect Me - LinkedIn\nWhy I Created This\nYou know, algorithms are just one line of code away, but the\nreal learning\nbegins when you understand\nhow and when\nto use them.\nThat’s what\nThe ML Diaries\nis all about — making machine learning approachable, structured, and meaningful.\n#\nlist\n#\ncontent\n#\narticles\n#\nmachine-learning\n#\ntutorial\n#\npython\n83\nviews",
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