Daan Kolkman’s Post

🌟 Understanding Causal Inference in Data Science: Moving Beyond Correlation. If you’ve ever worked with data, you’ve probably heard the saying, “Correlation does not imply causation.” In simple terms, just because two things happen together doesn’t mean one caused the other. In data science, we often find patterns in data, but the real challenge is figuring out what actually causes what. That’s where causal inference comes in! 🤔 So, What is Causal Inference? Causal inference is all about answering deeper questions like, “Why did this happen?” or “What would happen if we changed something?” Unlike traditional machine learning, which focuses on predicting outcomes based on patterns, causal inference helps us understand the why behind those outcomes. 🛠️ Why Does This Matter? Think about it: In healthcare, instead of just seeing that two things are related (like exercise and lower blood pressure), we want to know if exercising more actually causes lower blood pressure. In marketing, rather than just observing that a new campaign coincided with higher sales, we want to understand if the campaign caused the increase in sales. 🔍 How Do We Do It? Some popular techniques in causal inference are: 1. Randomized Controlled Trials (RCTs): Like A/B testing, where you randomly split a group to see what works better. 2. Matching Methods: Pairing similar individuals to compare outcomes more fairly. 3. Causal Diagrams (DAGs): Visual maps that help us understand what factors might be influencing each other. 4. Counterfactuals: This is like asking, "What if we had done things differently?" 🚀 Combining Machine Learning with Causal Inference: Now, we’re seeing more advanced methods that combine machine learning with causal inference. This means we’re not just building models that predict but also ones that help us understand the impact of our decisions. Let’s demystify data science together! If you’re exploring causal inference or curious about how it works, I’d love to connect and chat more about it. #DataScience #MachineLearning #CausalThinking #AI #Analytics #CuriousMinds #LearningTogether

#silentlesson 🔍 24 Data Science Terms Explained! 🔍 Understanding data science is crucial in today’s data-driven world. Here's a quick guide to essential terms you should know: 1️⃣ A/B Testing: Comparing two versions to determine the better one. 2️⃣Algorithm: Step-by-step procedure for calculations. 4️⃣ Artificial Intelligence (AI): Machines performing tasks like human intelligence. 4️⃣ Big Data: Complex data sets beyond traditional handling. ...and more, including Clustering, Deep Learning, and Predictive Analytics! Ready to dive deeper? Explore these resources to expand your knowledge: - [Coursera Data Science Courses](https://lnkd.in/dpSaeytR) - [Kaggle Learn](https://lnkd.in/d5H7GZ5B) - [Towards Data Science Articles](https://lnkd.in/dGAcGrwi) - [DataCamp](https://www.datacamp.com/) Empower yourself with the knowledge to drive innovation and insights! 🚀 #DataScience #AI #MachineLearning #BigData #Analytics #Innovation

Abhijit S. Yogesh Singh Gurvinder Singh helping enterprise teams transform data into strategic advantages. Read more 👇

Just finished the course “Machine Learning and AI Foundations: Prediction, Causation, and Statistical Inference” by Keith McCormick! Check it out: https://lnkd.in/gTesCMzN #machinelearning #artificialintelligence.

🚀 Linear Regression Simplified: Closed-Form vs. Gradient Descent 🚀 When it comes to #LinearRegression, two techniques often come up—Closed-Form Solution and Gradient Descent. But which one should you use and why? 🤔 Let’s dive in! 🔹 Closed-Form Solution (Normal Equation) This approach solves for the best-fit line in one step—perfect for smaller datasets where you want quick, exact results. Think of it as a “plug-and-play” formula. ✅ Pros: Fast and precise for small datasets ❌ Cons: Not scalable for larger datasets; computation-heavy with many features. 🔹 Gradient Descent For big data, Gradient Descent is the go-to. Instead of a one-shot solution, it uses iterative steps to minimize error, making it flexible for datasets with thousands of features. ✅ Pros: Scalable and efficient for large data ❌ Cons: Slower convergence and requires tuning the learning rate for accuracy. 💼 So, which one should you choose? For smaller datasets with fewer features: Closed-Form Solution is efficient! For larger, high-dimensional datasets: Gradient Descent shines, handling scalability smoothly. Both methods are invaluable for #DataScientists, equipping you with the adaptability to tackle diverse data challenges. Mastering them means you’re ready for anything from clean data modeling to tackling complex, large-scale problems! 🔍📊 Which approach do you use most often? Let’s discuss below! 👇 #MachineLearning #DataScience #DataAnalysis #AI #GradientDescent #CareerInData

Just finished the course “Machine Learning and AI Foundations: Prediction, Causation, and Statistical Inference” by Keith McCormick! Check it out: https://lnkd.in/ezMkPv4n #machinelearning #artificialintelligence.

Just finished the course “Machine Learning and AI Foundations: Prediction, Causation, and Statistical Inference” by Keith McCormick! Check it out: https://lnkd.in/eipAyEX9 #machinelearning #artificialintelligence.

Are you struggling to pick between deep learning & traditional methods for data analysis? Understand your goals & data. Deep learning suits big, complex data, while traditional methods work for smaller sets. Consider needs & resources. Choose wisely! Follow Xminds: https://lnkd.in/gvPNr24H #DataAnalysis #DeepLearning #TraditionalMethods #xminds

How to Ensure Your Machine Learning Model Performs Like a Pro on New Data? 🤔 . 💡 Cross-validation 🔍 A Quick Breakdown: 1️⃣ 𝗗𝗮𝘁𝗮 𝗦𝗽𝗹𝗶𝘁𝘁𝗶𝗻𝗴: 𝙋𝙪𝙧𝙥𝙤𝙨𝙚: Prevent overfitting by using different subsets for training and testing. 𝙃𝙤𝙬: Divide the dataset into k folds (e.g., 5-folds). Each fold tests the model once. 2️⃣ 𝗠𝗼𝗱𝗲𝗹 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴: 𝙋𝙪𝙧𝙥𝙤𝙨𝙚: Helps the model generalize rather than memorize. 𝙃𝙤𝙬: Train on k-1 folds and test on the remaining folds. Repeat this k times. 3️⃣ 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗔𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴: 𝙋𝙪𝙧𝙥𝙤𝙨𝙚: Provides a robust estimate of model performance. 𝙃𝙤𝙬: Average results from each fold for a reliable performance metric. 4️⃣ 𝗠𝗼𝗱𝗲𝗹 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻: 𝙋𝙪𝙧𝙥𝙤𝙨𝙚: Choose the best model and fine-tune hyperparameters. 𝙃𝙤𝙬: Use averaged metrics to compare models and adjust settings. 🚀 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: 𝙂𝙚𝙣𝙚𝙧𝙖𝙡𝙞𝙯𝙖𝙩𝙞𝙤𝙣: Ensures your model performs well on new, unseen data. 𝙍𝙤𝙗𝙪𝙨𝙩𝙣𝙚𝙨𝙨: Builds reliable models for real-world applications. Don’t overlook cross-validation—it’s a game-changer for building high-performing models! Follow me for data science tips that’ll make your algorithms smarter and your coffee stronger! ☕🤓🚀 #MachineLearning #DataScience #CrossValidation #AI #ModelEvaluation #DataScienceTips

Just finished the course “Machine Learning and AI Foundations: Prediction, Causation, and Statistical Inference” by Keith McCormick! Check it out: https://lnkd.in/gf3yYH6h #machinelearning #artificialintelligence.