Matthew Heaton’s Post

🎓 Unlock the Secrets of Math in Data Science and AI! 🧮 Are you wondering how much math is really needed to excel in Data Science and Artificial Intelligence? 🤔 Join my upcoming webinar where we’ll break it all down: ✅ The essential math skills you need ✅ Real-world examples of how math powers AI and data-driven insights ✅ Tips for mastering mathematical concepts like linear algebra, calculus, and statistics Whether you’re a beginner or looking to level up your skills, this session will provide practical insights to guide your journey. 📅 Date: 28-Nov-2024 ⏰ Time: 8 Pm 🌐 Register here: https://lnkd.in/gtXxxSu5 Let’s simplify math for Data Science and AI success! 🚀 👉 Don’t forget to share with your network if they’re curious about this exciting topic. sudhanshu kumar Ashi kaur Chandok Dibyanshu Kumar Boktiar Ahmed Bappy #DataScience #ArtificialIntelligence #MathForAI #Webinar #LearningLogi

🎓 Unlock the Secrets of Math in Data Science and AI! 🧮 Are you wondering how much math is really needed to excel in Data Science and Artificial Intelligence? 🤔 Join my upcoming webinar where we’ll break it all down: ✅ The essential math skills you need ✅ Real-world examples of how math powers AI and data-driven insights ✅ Tips for mastering mathematical concepts like linear algebra, calculus, and statistics Whether you’re a beginner or looking to level up your skills, this session will provide practical insights to guide your journey. 📅 Date: 28-Nov-2024 ⏰ Time: 8 Pm 🌐 Register here: https://lnkd.in/gtXxxSu5 Let’s simplify math for Data Science and AI success! 🚀 👉 Don’t forget to share with your network if they’re curious about this exciting topic. sudhanshu kumar Ashi kaur Chandok Dibyanshu Kumar Boktiar Ahmed Bappy #DataScience #ArtificialIntelligence #MathForAI #Webinar #LearningLogic

Data Science as a Superhero Team 🚀 Sometimes I like to imagine fun things, like: what if Data Science was a team of superheroes? Here’s how I picture it: 1. Math: The Mind Reader 🧠 Math is the team’s mind reader, helping us understand what’s going on beneath the surface. Whether it's transforming data with algebra, optimizing models with calculus, or predicting outcomes with probability, Math helps us see things we wouldn’t normally notice. 2. Computer Science: The Muscle 💪 Computer Science is the strength of the team. Without it, all that math would just be theory. Computer Science gives us the tools to handle tons of data, build models, and run algorithms at lightning speed. 3. Statistics: The Detective 🕵️♂️ Statistics is the detective. It spots patterns, reveals hidden truths, and helps us figure out what’s real and what’s just noise. Statistics is why we can trust our models (most of the time) and not just guess. But here’s the thing: 🔹 If you only rely on Math, you’ll have fancy equations but no clue how to use them. 🔹 If you focus only on Computer Science, you might build something fast… but it could be wrong. 🔹 If you only use Statistics, you’ll have great insights but no way to handle massive data. To be the true superhero, you’ve got to master all three! 💥 Subscribe to our newsletter: https://lnkd.in/eW_EY-7p Don’t forget to follow Badreddine Chaguer to keep learning about #AI, #ML, #DataScience, and #GenAI

Mastering the Building Blocks of #Data_Science: Why #Algebra Still Matters In the world of data science, we often talk about cutting-edge algorithms, machine learning models, and large datasets. But at the foundation of all these complex systems lies something very simple: Algebra. The formulas in this image may look like high school algebra, but they are essential building blocks in the data science journey. Whether you're performing feature engineering, optimizing a model, or analyzing relationships in data, algebra plays a crucial role. Here's how: Polynomial Regression: Notice the familiar (a+b)2(a + b)^2(a+b)2 and (a−b)2(a - b)^2(a−b)2 formulas? These are essential for understanding polynomial terms, which can capture non-linear patterns in regression. Matrix Operations: Many data operations rely on matrix algebra, which uses identities like a2+b2=(a+b)2−2aba^2 + b^2 = (a + b)^2 - 2aba2+b2=(a+b)2−2ab to simplify complex transformations in models and optimizations. Gradient Descent and Loss Functions: Expanding expressions and recognizing patterns in algebraic formulas can help in deriving gradients and understanding loss functions, key steps in training machine learning models. Combinatorial Problems: Algebraic expansions aid in combinatorial calculations, which can be useful for feature selection, probability calculations, and working with combinations of variables. So, if you’re diving into data science or machine learning, don’t skip over the basics! Revisiting algebra can help sharpen your skills, making it easier to understand advanced topics down the road. 💡 What’s your take? How often do you use algebra in your data science #projects? #DataScience #MachineLearning #Algebra #Mathematics #DataScienceBasics #FeatureEngineering #LinkedInLearning #MachineLearningModels

#letstalk Recently, I've been revisiting statistics and probability. Yep, I learned this stuff in college, but revisiting it now gives me a fresh perspective. These rules and formulas feel like they're speaking to me differently. Being a data science student means much more than just making machine learning models and using Large language models. It's a lifestyle, and I'm really getting into it. Let me give you an example. We often study distributions like normal, log-normal, and others. Similarly, there's one called the Pareto distribution (Power Law Distribution). While many might dismiss it as just another distribution, let me connect it with your everyday life. The Pareto distribution follows the 80-20 rule, which says that "80% of results come from 20% of efforts." Isn't that interesting? We all know it's not limited to this distribution alone; in our lives, most of the results often come from a small amount of effort. This is just one example of how data science affects our daily lives. Data science is incredibly vast and fascinating. Diving into this world has given me a new perspective on life. We, the ones who make assumptions, are always looking for probabilities and percentages in everything. And yes, we often estimate and assume, but there's an art to it, and we're pretty good at it. #datascience #statistics #probability #mathematics #letsconnect #letsupgrade

For all those who have been asking for resources to get started in data science, check out my blog: https://lnkd.in/gnjeH89j PS: I am moving all my long-form content to Substack, so if you aren't subscribed to it already, do it now :) https://lnkd.in/gzgg_PUt I am also sharing latest updates, free resources, and answering any of your questions on my AI with Aish Telegram group: https://t.me/ai_with_aish

Let me tell you something I wish someone had told me when I started out in data science… 𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗺𝗲𝗺𝗼𝗿𝗶𝘇𝗲 𝗲𝘃𝗲𝗿𝘆 𝗺𝗮𝘁𝗵 𝗳𝗼𝗿𝗺𝘂𝗹𝗮 𝘁𝗼 𝘀𝘂𝗰𝗰𝗲𝗲𝗱. I know, because I’ve been there—feeling overwhelmed, thinking I needed to know every equation by heart to be taken seriously. But here’s the truth: Real data science is not about memorizing every formula. It’s about knowing how to solve problems, make sense of data, and apply the right tools at the right time. 𝗧𝗵𝗲 𝘄𝗮𝘆 𝗜 𝘁𝗲𝗮𝗰𝗵 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝘀 𝗯𝘂𝗶𝗹𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝗿𝗲𝗹𝗮𝘁𝗮𝗯𝗹𝗲 𝘀𝘁𝗼𝗿𝗶𝗲𝘀—the kind that make complex ideas click. I want you to actually see where each technique fits in the real world so you can approach data science with confidence and clarity. Here’s what I’ve learned from experience: - 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗸𝗻𝗼𝘄𝗶𝗻𝗴 𝘄𝗵𝗲𝗻 𝗮𝗻𝗱 𝘄𝗵𝘆, 𝗻𝗼𝘁 𝗲𝘃𝗲𝗿𝘆 𝗱𝗲𝘁𝗮𝗶𝗹. Instead of memorizing, learn when certain techniques work best. Know why you’re choosing linear regression or clustering. The “when” and “why” will guide you way better than memorizing every formula. - 𝗨𝘀𝗲 𝘁𝗼𝗼𝗹𝘀 𝘁𝗼 𝗱𝗼 𝘁𝗵𝗲 𝗵𝗲𝗮𝘃𝘆 𝗺𝗮𝘁𝗵. Professionals rely on Python, R, and libraries to handle complex math for them. It’s like using a calculator—no one expects you to do it all in your head. - 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗼𝘃𝗲𝗿 𝗽𝗲𝗿𝗳𝗲𝗰𝘁𝗶𝗼𝗻. Knowing the purpose behind a method will help you make better decisions than trying to remember every formula. When you understand the logic, you can apply it with confidence—no second-guessing. 𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗽𝗮𝗿𝘁? This approach frees you up to actually enjoy the journey. Instead of feeling bogged down by endless formulas, you’ll get to focus on solving real problems, building projects, and letting your skills shine—without being held back by the fear of not knowing it all. So if you’re feeling stressed about mastering every formula, let go of that pressure. 𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝗮 𝗽𝗮𝘁𝗵 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗼𝗻 𝗿𝗲𝗮𝗹 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴, 𝘀𝘁𝗼𝗿𝗶𝗲𝘀, 𝗮𝗻𝗱 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝘆𝗼𝘂’𝗹𝗹 𝗳𝗶𝗻𝗱 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝗴𝗿𝗼𝘄𝗶𝗻𝗴 𝗳𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝗲𝘃𝗲𝗿. P.S Grasp complex concepts with ease, download my free guide and start your AI/ML journey today! → https://bit.ly/4cCCH7L #data #MachineLearning #DeepLearning #AI #DataScience

#AI #ML #Tech Expand Your Data Science Toolkit with Our Latest Math and Stats Must-Reads: Feeling inspired to write your first TDS post? We’re always open to contributions from new authors. The fundamental principles of math that data scientists use in their day-to-day work may have been around for centuries, but that doesn’t mean we should approach the topic as if we only learn it once and then store away our knowledge in some dusty mental attic. Practical approaches, tools, and use cases evolve all the time—and with them comes the need to stay up-to-date. This week, we’re thrilled to share a strong lineup of recent math and stats must-reads, covering a wide range of questions and applications. From leveraging (very) small datasets to presenting linear regressions in accessible, engaging ways, we’re sure you’ll find something new and useful to explore. Let’s dive in! * N-of-1 Trials and Analyzing Your Own Fitness Data The idea behind N-of-1 studies is that you can draw meaningful insights even when the data you’re using is based on input from a single person. It has far-reaching potential for designing individualized healthcare strategies, or, in the case of Merete Lutz’s fascinating project, establishing meaningful connections between alcohol consumption and sleep quality. * How Reliable Are Your Time Series Forecasts, Really? Making long-term predictions is easy; making accurate long-term predictions is, well, less so. Bradley Stephen Shaw recently shared a useful guide to help you determine the reliability horizon of your forecasts through the effective use of cross-validation, visualization, and statistical hypothesis testing. * Building a Math Application with LangChain Agents Despite the major strides LLMs have made in the past couple of years, math remains an area they struggle with. In her latest hands-on tutorial, Tahreem Rasul unpacks the challenges we face when we try to make these models execute mathematical and statistical operations, and outlines a solution for building an LLM-based math app using LangChain agents, OpenAI, and Chainlit. Photo by Chloe Frost-Smith on Unsplash * A Proof of the Central Limit Theorem It’s always a joy to see an abstract concept take concrete shape and, along the way, become much more accessible and intuitive for learners. That’s precisely what Sachin Date accomplishes in his latest deep dive, which shows us the inner workings of the central limit theorem, “one of the most far-reaching and delightful theorems in statistical science,” through the example of… candy! * 8 Plots for Explaining Linear Regression to a Layman Even if you, a professional data scientist or ML engineer, fully grasp the implications of your statistical analyses, chances are many of your colleagues and other stakeholders won’t. This is where strong visualizations can make a major difference, as Conor O'Sullivan… #MachineLearning #ArtificialIntelligence #DataScience

If you're interested in machine learning, be sure to check out my posts. Each day I post a ML mini-lesson on the most useful techniques for real-world data science. Wanna know the best part? They're designed for any professional. No advanced math required. A new machine learning mini-lesson will be posted today. You can see past ML mini-lessons via my profile. Stay healthy and happy data sleuthing!

🌟 Learn with me: Supervised Learning - Linear Regression with Multiple Features! 🌟 I'm diving into Supervised Learning concepts, starting with Linear Regression and exploring the details of multi features models! Here’s a quick recap of the essential building blocks for anyone interested in understanding how to tackle real-world prediction tasks 📊: 🔹 Cost Function - Understand how the error is calculated so we can minimize it effectively! It's the first step toward building a model that predicts accurately 🎯. 🔹 Gradient Descent - This optimization algorithm is our guide toward the lowest error by iteratively adjusting weights 🚶♂️➔. 🔹 Gradient Descent vs. Normal Equation - Discover the advantages of each! Choose the right approach based on your data size and efficiency needs ⚖️. 🔹 Feature Scaling - Prepping our data by standardizing feature ranges helps gradient descent converge faster 🚀. 🔹 Feature Engineering - Craft meaningful features that truly represent the data and help improve the model’s predictive power 🔧. 🔹 Polynomial Function - Capture more complex relationships by adding polynomial features! Great for fitting nonlinear data 📈. Inspired by the Supervised Learning course from deeplearning.ai and Stanford University, I’m excited to keep sharing these insights! Let’s keep learning and growing together in data science 🌍🤓. #LearnWithMe #DataScienceJourney #MachineLearning #SupervisedLearning #LinearRegression #deeplearningai #StanfordUniversity GitHub: https://lnkd.in/ddZDVKuU