No Free Lunch Theorem: Why No Model Wins Everywhere

🕵️ The Detective's Rulebook Stop guessing the cause, start calculating it! 🛑 Bayes' Theorem is arguably the most powerful piece of math in Data Science. It’s the formula that allows an AI system to rationally update its belief in a scenario as new evidence arrives. Yesterday, we learned P(Effect|Cause). Today, Day 29 of the Data Science Theory bootcamp, we use Bayes' Theorem to calculate the crucial reversal: P(Cause|Effect). Why this is a game-changer for your career: 👉 Medical Diagnostics: What is the probability of a disease given a positive test result? 👉 Spam Filtering: What is the probability of spam given a specific keyword is present? 👉 Predictive AI: It provides the theoretical foundation for Naive Bayes Classifiers and modern Bayesian A/B Testing. Mastering Bayes' Theorem means you understand that Posterior Probability is proportional to Likelihood times Prior. You combine your initial knowledge with the strength of new evidence to get a continuously better, more accurate prediction. ➡️ Ready to understand the engine of rational, evidence-based prediction? Dive into the full post and challenge now! Click the link to master Bayes' Theorem: 🔗 https://lnkd.in/daXei8BG #DataScienceTheory #BayesTheorem #Probability #StatisticsForDS #MachineLearningConcepts #AIExplained #PriorAndPosterior #SholaAjayi

🔬 The Engine of Contextual Prediction The key to building smarter AI? It’s all about context. 🔑 Simple probability is a guess. Conditional Probability, P(A|B), is an informed guess. It’s what allows Data Scientists to shift from "What's the overall chance of fraud?" to the critical question: "What is the chance of fraud GIVEN the user is making a purchase in an unusual country?" This ability to leverage prior knowledge is the true engine of modern predictive systems, from Fraud Detection to Predictive Maintenance. Today is Day 28 of the Data Science Theory bootcamp, and we're mastering this essential concept: 👉 The Formula: P(A|B) = P(A ⋂ B) / P(B), calculating the intersection relative to the condition. 👉 The Why: Understanding how context restricts the sample space, leading to more accurate, actionable predictions. 👉 The Bridge: Conditional Probability is your direct path to understanding Bayes' Theorem (our next step!) and advanced classification algorithms. If you want to move beyond general averages and start assessing real-time risk, this lesson is non-negotiable. ➡️ Ready to level up your predictive thinking? Dive into the full post and challenge now! Click the link to master Conditional Probability: 🔗 https://lnkd.in/dAUCVBnS #DataScienceTheory #ConditionalProbability #StatisticsForDS #MachineLearningConcepts #BayesTheorem #AIExplained #RiskAssessment #SholaAjayi

📜 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗕𝗮𝗰𝗸 𝗦𝘂𝗻𝗱𝗮𝘆 𝗙𝗿𝗼𝗺 𝗦𝗵𝗼𝗿𝘁𝗲𝘀𝘁 𝗣𝗮𝘁𝗵𝘀 𝘁𝗼 𝗦𝗺𝗮𝗿𝘁 𝗚𝗿𝗮𝗽𝗵𝘀: 𝗛𝗼𝘄 𝗗𝗶𝘀𝘁𝗮𝗻𝗰𝗲 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗦𝗵𝗮𝗽𝗲𝗱 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗪𝗼𝗿𝗹𝗱 Once upon a time in 1956, a Dutch computer scientist named Edsger W. Dijkstra designed an algorithm to find the shortest path between two nodes. Simple idea, right? But that one spark of logic became the backbone of modern data connectivity — powering everything from 𝗚𝗼𝗼𝗴𝗹𝗲 𝗠𝗮𝗽𝘀 to 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻’𝘀 “𝗣𝗲𝗼𝗽𝗹𝗲 𝗬𝗼𝘂 𝗠𝗮𝘆 𝗞𝗻𝗼𝘄”. Let’s connect the dots 🕸️👇 🔹 𝗧𝗵𝗲𝗻:  • Dijkstra’s algorithm, Breadth-First Search (BFS), and Floyd-Warshall were mathematical curiosities in graph theory textbooks.  • They solved pathfinding problems on paper networks — roads, circuits, or cities. 🔹 𝗡𝗼𝘄:  • They’re used in knowledge graphs, recommendation systems, fraud analytics, and vector databases.  • Tools like Neo4j, TigerGraph, and GraphFrames (Spark) use these algorithms at scale to find semantic distance, not just physical distance.  • Even Large Language Models (LLMs) rely on embedding similarity, which is essentially a distance metric in high-dimensional space. 🔹 𝗡𝗲𝘅𝘁: The future blends 𝗴𝗿𝗮𝗽𝗵 + 𝗔𝗜, where distance means context, and every connection tells a story — of people, data, and meaning. ✨ 𝗟𝗲𝘀𝘀𝗼𝗻: Distance algorithms taught us that data is more valuable when connected. In graphs — like in life — the shortest path isn’t always the most insightful one. What’s your favorite use case of graph-based distance algorithms? 🚀 A useful link to quench your curiosity - https://lnkd.in/ggEpawi9 #LookingBackSunday #GraphDatabases #DataEngineering #Neo4j #AI #Algorithms #DataScience #GraphTheory

Apparently, when you google RBF, the top result is Resting Bitch Face 💀 But in data science land — and especially for those of us working with Bayesian or kernel-based models — RBF stands for Radial Basis Function. And it’s way more charming than its acronym suggests. At its core, an RBF measures similarity between two points: if two points are close, it tends to 1, if they’re far apart, it drops toward 0. Classic example — Gaussian. That distance-based smoothness is what makes RBFs so useful for capturing subtle, non-linear patterns, you can even stack them. RBFs actually power a lot of what we do: 🔔Gaussian Processes and Bayesian modeling (check out PyMC for a great implementation) ➗SVMs where they’re the classic kernel (remember that kernel trick when you “lift” data into a high-dimensional space and get the dot product there for free?) 🕸️Even neural networks, where RBFs can act as hidden activations So next time your data refuses to behave linearly — give it an RBF. This little appreciation post is inspired by Wesley Boelrijk's Bayesian modeling talk at PyData Amsterdam. #DataScience #BayesianModeling #MachineLearning #PyMC #Halloween

Seems like there is al ot of research around moving model reasoning from Linear to more of a recursive model. The paper this morning talks about how the they loop over "latent space" (think the already trained data) it loops over that data until it reach the probability of greater than .5 that the answer is correct. The big advantage of this is there is no token usage. the disadvantage is it cant really learn over time. So probably will end up being some kind of hybrid/ What do you think about this approach? Do you really think anything we are doing now will be the "big break through"? The ELI5 summary Instead of making a giant brain, they made a brain that thinks twice with the same neurons. It looks at a question, pauses, quietly re-checks its thoughts inside itself a few times, and then answers. It learns when to stop thinking — so it doesn’t waste time on easy stuff. Here is the paper: https://lnkd.in/gnGS_Dhe

When machine learning models are used to guide decisions in energy, finance, or infrastructure, one question becomes crucial: how certain are their predictions? At the Hi! PARIS Summer School, Aymeric Dieuleveut (École Polytechnique) unpacked this issue through the lens of conformal prediction, a method that turns single-point forecasts into interpretable ranges, showing not just what a model predicts, but how confident it is. His message was clear: understanding uncertainty isn’t a technical afterthought, it’s central to building #AI systems we can trust. 👉 Watch more: https://lnkd.in/epeEm-Jf #MachineLearning #TrustworthyAI #ConformalPrediction #HiPARIS #AITransparency #DataScience #France2030

🚀 Another step forward in the master’s programme in AI Engineering I am taking with ProfessionAI! This module focused on Advanced Machine Learning Algorithms, blending theory and hands-on practice with Python and Scikit-learn. Throughout the module, we explored and implemented key machine learning techniques: 📌 Optimization Algorithms 🔹 Gradient Descent (full batch, stochastic, and mini-batch) 🔹 Model parameters tuning and evaluation 📌 Advanced Machine Learning Algorithms 🔹 K-Nearest Neighbors and Radius Neighbors 🔹 Support Vector Machines: linear and kernel-based 🔹 Naive Bayes: Gaussian, Bernoulli, Multinomial, Categorical 🔹 Tree-based models: Decision Trees and Random Forests 🔹 Intro to Neural Networks: MLP, ReLU, tanh, step functions 📌 Model Selection and Comparison 🔹 Parametric vs. non-parametric models 🔹 Strengths, limitations, and use-case considerations 📌 Final Project – Classification of Exotic Fruits For the final project, I developed a K-Nearest Neighbors classification model to predict fruit types based on physical characteristics. Key steps included data exploration and preprocessing, feature standardization, training/test split, hyperparameter optimization using accuracy & log-loss, and performance evaluation through confusion matrix and classification report. 💡 Working hands-on with machine learning algorithms makes me appreciate even more how crucial technical understanding is to build responsible and trustworthy AI. Deepening my skills in model behaviour, accuracy, and limitations helps me approach AI governance and ethics not just from a conceptual standpoint, but with the clarity needed to understand real systems. The project is available on my GitHub repository here: https://lnkd.in/dXiqKBhm Feel free to check it out!   #MachineLearning #Python #Pandas #NumPy #ScikitLearn #DataPreprocessing #KNN #Classification #AIEngineering #ArtificialIntelligence #AIEthics #ProfessionAI 

🤖 Implementation of Support Vector Machine (SVM) in Data Science As part of my data science practical work, I implemented a Support Vector Machine (SVM) model to classify and analyze data effectively. This exercise deepened my understanding of supervised machine learning algorithms and how SVMs can separate data points across different classes with high accuracy. Through this task, I gained valuable insights into model training, evaluation, and the importance of parameter tuning for achieving optimal performance. #DataScience #MachineLearning #SVM #AI #ModelBuilding #LearningExperience guidance by:Ashish Sawant GitHub:https://lnkd.in/gwTi87fU

29/90:𝑵𝒐𝒕 𝑬𝒗𝒆𝒓𝒚 𝑫𝒂𝒕𝒂 𝑺𝒕𝒐𝒓𝒚 𝑺𝒕𝒂𝒓𝒕𝒔 𝒘𝒊𝒕𝒉 𝒂 𝑴𝒐𝒅𝒆𝒍; 𝑺𝒐𝒎𝒆𝒕𝒊𝒎𝒆𝒔 𝑰𝒕 𝑺𝒕𝒂𝒓𝒕𝒔 𝒘𝒊𝒕𝒉 𝒂 𝑮𝒐𝒐𝒅 𝑺𝒂𝒎𝒑𝒍𝒆. I know I’ve been sharing a lot about machine learning lately (neural networks, convergence plots, architectures) all the exciting stuff, but beyond the models, I’ve also been exploring advanced statistics, and it’s been such an eye-opening journey. In one of my 𝑴𝑨𝑻𝑯-5381 𝒂𝒔𝒔𝒊𝒈𝒏𝒎𝒆𝒏𝒕𝒔, we explored the backbone of every solid research or data project 𝒔𝒂𝒎𝒑𝒍𝒊𝒏𝒈 𝒎𝒆𝒕𝒉𝒐𝒅𝒔 𝒂𝒏𝒅 𝒆𝒙𝒑𝒆𝒓𝒊𝒎𝒆𝒏𝒕𝒂𝒍 𝒅𝒆𝒔𝒊𝒈𝒏. From identifying 𝒔𝒐𝒖𝒓𝒄𝒆𝒔 𝒐𝒇 𝒃𝒊𝒂𝒔 𝒊𝒏 𝒔𝒖𝒓𝒗𝒆𝒚𝒔 (like nonresponse, selection, and self-report bias) to analyzing factors, treatments, replications, and blocks in experiments, it really put things into perspective. It’s fascinating how much these “simple” concepts shape the reliability of our insights. For instance, the way you𝒔𝒆𝒍𝒆𝒄𝒕 𝒚𝒐𝒖𝒓 𝒔𝒂𝒎𝒑𝒍𝒆 𝒐𝒓 𝒔𝒕𝒓𝒖𝒄𝒕𝒖𝒓𝒆 𝒚𝒐𝒖𝒓 𝒆𝒙𝒑𝒆𝒓𝒊𝒎𝒆𝒏𝒕 can completely change the story your data tells. 𝐀 𝐪𝐮𝐢𝐜𝐤 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲: Before feeding data into a model, ask yourself; 𝑾𝒂𝒔 𝒕𝒉𝒊𝒔 𝒅𝒂𝒕𝒂 𝒄𝒐𝒍𝒍𝒆𝒄𝒕𝒆𝒅 𝒇𝒂𝒊𝒓𝒍𝒚 𝒂𝒏𝒅 𝒓𝒆𝒑𝒓𝒆𝒔𝒆𝒏𝒕𝒂𝒕𝒊𝒗𝒆𝒍𝒚? If the foundation is biased, no algorithm can truly “fix” that. Working through that assignment reminded me that data science isn’t just about coding or accuracy metrics ; it’s about 𝒄𝒓𝒊𝒕𝒊𝒄𝒂𝒍 𝒕𝒉𝒊𝒏𝒌𝒊𝒏𝒈, 𝒈𝒐𝒐𝒅 𝒅𝒆𝒔𝒊𝒈𝒏, 𝒂𝒏𝒅 𝒂𝒔𝒌𝒊𝒏𝒈 𝒕𝒉𝒆 𝒓𝒊𝒈𝒉𝒕 𝒒𝒖𝒆𝒔𝒕𝒊𝒐𝒏𝒔 𝒇𝒓𝒐𝒎 𝒕𝒉𝒆 𝒗𝒆𝒓𝒚 𝒃𝒆𝒈𝒊𝒏𝒏𝒊𝒏𝒈. 💡 𝓔𝓋𝓮𝓃 𝓉𝒽𝓮 𝓂𝓸𝓈𝓉 𝒶𝒹𝓋𝒶𝓃𝒸𝓮𝒹 𝓂𝓸𝒹𝓮𝓁 𝒸𝒶𝓃’𝓉 𝒻𝒾𝓍 𝒻𝓁𝒶𝓌𝓮𝒹 𝒹𝒶𝓉𝒶. Concepts like 𝒓𝒂𝒏𝒅𝒐𝒎𝒊𝒛𝒂𝒕𝒊𝒐𝒏, 𝒓𝒆𝒑𝒍𝒊𝒄𝒂𝒕𝒊𝒐𝒏, 𝒂𝒏𝒅 𝒔𝒕𝒓𝒂𝒕𝒊𝒇𝒊𝒆𝒅 𝒔𝒂𝒎𝒑𝒍𝒊𝒏𝒈 might not sound as glamorous as neural networks, but they’re what make every prediction meaningful. For me, it was a humbling reminder that 𝒔𝒕𝒓𝒐𝒏𝒈 𝒎𝒐𝒅𝒆𝒍𝒔 𝒄𝒐𝒎𝒆 𝒇𝒓𝒐𝒎 𝒔𝒕𝒓𝒐𝒏𝒈𝒆𝒓 𝒎𝒆𝒕𝒉𝒐𝒅𝒔 and that true expertise in ML means understanding the statistics that ground it all. #DataAnalytics #AdvancedStatistics #SamplingMethods #ExperimentalDesign #MachineLearning #QuantitativeMethods #ContinuousLearning #GrowWithIsabella

ML doesn’t learn. Data science isn’t science. Agents lack agency. But sure, let’s keep calling it Artificial Intelligence. Because Artificial Understanding doesn’t sound as cool. 😉