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Devashish Belwal
Hey I am on Medial • 25d
🧠 From Linear Thinking to Non-Linear Solutions: My ML Journey Just wrapped up a major milestone in my data science learning - transitioning from Logistic Regression to Support Vector Machines through Krish Naik's bootcamp. 🔍 The Learning Curve: Logistic Regression Breakthrough: Initially thought it was just "linear regression for classification" - completely wrong! The real insight came when I understood how the sigmoid function transforms everything. We're not drawing lines, we're predicting probabilities. That shift in thinking was crucial. ROC Curves - The Game Changer: Moved beyond the "accuracy = success" mindset. Understanding true positive vs false positive rates opened up a whole new dimension of model evaluation. Context matters more than I realized. SVMs - Where Math Gets Exciting: This is where things clicked! The concept of kernels transforming data into higher dimensions to find optimal separating hyperplanes is fascinating. The mathematical intuition is challenging but incredibly rewarding. 📊 Current Status: More confident implementing SVMs than logistic regression Ready to tackle advanced ML concepts Mathematical foundations getting stronger daily 🚀 Key Takeaway: Each concept builds on the previous one. The learning compounds exponentially when you understand the "why" behind each algorithm, not just the "how." What's Next: Diving deeper into advanced ML topics. The foundation is solid, time to build higher! Question for the community: What's been your biggest breakthrough moment in transitioning from basic to intermediate ML concepts? Tags: #MachineLearning #DataScience #SVMs #LogisticRegression #TechLearning #StartupSkills #AIUpskilling Always learning, always building 💪
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