Learn Math for Data Science with Dr. David Hoyle at ODSC AI West

🚀 Machine Learning and Deep Learning Cheat Sheet - By Stanford & MIT Many dive straight into ML algorithms - but the real experts start with the math. Stanford and MIT emphasise mastering the foundations first, building intuition before application. 🧠 The Learning Path: 1. Probability & Statistics - Understanding uncertainty. 2. Linear Algebra - Powering every ML and DL model. 3. Optimisation - Where models truly learn. 4. Machine & Deep Learning Models - Built on a strong mathematical base.

Thrilled to Participate in the ML Workshop: From Basics to Deployment by Innomatics Research Labs . Over the past few days, I had the opportunity to deepen my understanding of Machine Learning — right from core concepts to real-world deployment. This workshop was a great blend of theory and hands-on practice. 📚 Key Highlights of the Workshop: ✅ Day 1: Introduction to Machine Learning – Concepts & Applications ✅ Day 2: Data Preprocessing – Cleaning, Handling Missing Values & Feature Scaling ✅ Day 3: Core Algorithms – KNN, Naive Bayes, Regression, Decision Trees ✅ Day 4: Advanced Techniques – Bagging, Boosting, Random Forest & Model Evaluation ✅ Day 5: Deployment – Building and Deploying ML Models with Streamlit. This experience has strengthened my confidence in building ML models end-to-end. Excited to continue exploring and applying these skills in real-world projects! 🌟 #MachineLearning #DataScience #InnomaticsResearchLab #ModelDeployment #Upskilling #AIJourney

Old Math, Modern Tech: My Project on Finding Hidden Networks Just finished a fascinating project, and it's a perfect example of how 1970s math powers today's tech! 💡 The Problem: Ever wonder how platforms instantly spot "echo chambers" or new trends? They're finding hidden communities. A "community" is just a group of people (or nodes) in a network who are far more connected to each other than to the rest of the network. Think of a tight-knit friend group at a giant party. 🧑🤝🧑 My Project: I built a program that does exactly this using a machine learning method called Spectral Clustering. How It Works (The Simple Version): The algorithm doesn't just count connections; it looks for the structure. Think of it as finding the weakest seam in a piece of fabric 🧵, or the natural fault line in a map 🗺️. The algorithm mathematically finds the easiest place to "cut" the entire network into two distinct groups—the line that severs the fewest connections. This "weakest link" is almost always the border separating two main communities. How It Works (For the Techies 🤓): We're not really "cutting" anything. We're solving an eigenvalue problem. Build the Laplacian Matrix (L) for the graph. Solve the equation L * v = λ * v to find the eigenvectors. The "weakest seam" is revealed by the Fiedler Vector (the eigenvector for the second-smallest eigenvalue, λ). The sign (+/-) of the vector's components determines whether a person belongs to Community A or B. My Takeaway: It’s incredible that the solution to a modern big data problem isn’t some brand-new, complex AI, but a brilliant, elegant piece of linear algebra from 50 years ago. 📜➡️💻 It’s a great reminder that strong fundamentals are timeless. What's another "old-school" math concept you've seen work magic in modern tech? 👇 #MachineLearning #LinearAlgebra #DataScience #SpectralClustering #NetworkScience #TechExplained #Math #AI

✨ Week 10 — Mastering Supervised Learning This week, our team explored Supervised Learning, starting from foundational classification concepts to more advanced algorithms and evaluation techniques. We learned how different models identify patterns, make predictions, and support real-world decision-making. We began with the basics of binary classification using models. These models helped us understand how machine learning algorithms map features to categories and what makes each method unique. Moving into advanced supervised learning, we studied multiclass classification and powerful algorithms. We also deepened our knowledge of evaluation metrics and confusion matrices. Finally, we worked on Conqueror Project 1, applying a complete machine learning pipeline from Data Understanding and Preparation to Modeling, Evaluation, and Deployment. This hands-on experience strengthened our ability to build structured, reliable, and scalable ML workflows. 💡 Key takeaway: Supervised learning is more than picking a model — it’s about understanding data, choosing the right algorithm, evaluating performance properly, and building end-to-end solutions that solve real problems. 📊 Check out our Team ALGORYTHM slides for detailed explanations, visual diagrams, and practical examples from this week’s learning journey! #DigitalSkola #DataScience #LearningProgressReview

🚀 Master Engineering Mathematics Like a Pro! 💡 Here are some key flashcards every engineer must know — from Permutations & Combinations to Bayes’ Theorem, Eigenvalues, Mean & Variance, and the powerful Taylor Series expansion 🔥 These formulas aren’t just for exams — they’re the foundation of problem-solving, data science, AI, and real-world analytics. 📊💻 Keep this as your quick revision guide and strengthen your mathematical intuition every day! 🧠✨ Follow Mukesh Vishwakarma 👨🏼🎓 for more ... #EngineeringMathematics #Mathematics #DataScience #AI #MachineLearning #Statistics #EngineeringStudents #STEM #MathLovers #StudyMotivation #GeeksforGeeks #LearningNeverStops #TechCommunity #StudentLife #CareerGrowth

I had a great time attending NextMV’s webinar today with speakers Ryan O'Neil’Neil and Thiago Serra on practicing operations research and decision science in industry. My biggest takeaway was how different the real world can be from the academic problems we learn in class. I learned that strong optimization isn’t just about maximizing optimality but it’s about building models that real teams can understand, adopt, and run in production. I also really appreciated their perspective on how machine learning will continue to grow, and why fundamentals like linear algebra and data structures still matter even as AI evolves. I can't wait to apply what I learned today in future projects and eventually my career. #UCDavisMSBA #Analytics #Optimization #NextMv

🚀 Just rolled out a Maths for Data Science learning hub covering stats, linear algebra, and calculus—all in one streamlined dashboard. Huge thanks to Monal S. and Krish Naik for driving the foundation with their stellar course. Their insights fast-tracked this entire build. If you’re doubling down on data, ML, or AI and want a no-nonsense math refresher, this resource will accelerate your curve. 🔗 https://lnkd.in/g3VgAC8B No fluff. Just the core fundamentals you need to execute at scale. #DataScience #AI #Maths #MachineLearning #Upskill

One of the interesting things in learning modern AI is that in the beginning of many key methods, you have a scary looking math problem. For example, find an argmax from some math expectation over rewards over various probabilistic trajectories. Or some integral over distributions that we do not even know. But after several lines of math derivations and approximations, it comes to something we can actually use. A few tricks (multiply and divide on the same thing, or approximate something with normal distribution, or use gradient from a logarithm formular or some inequality) - and we have math foundations for algorithms we can use. Ok, sometimes - Monte Carlo sampling to the rescue. I guess scary looking math things we can not (yet) simplify into "practically solvable" would not get chapters in books ;) so this observation is biased, but still - nice to see humanity figured out so many things (and most of it - in the last 15 years)!

The Little Book of Deep Learning ✅ A guide that makes concepts click ↓ --- Here’s what you’ll learn: 1️⃣ Foundations ↳ Machine learning basics ↳ Computation efficiency ↳ Training methodologies 2️⃣ Deep Models ↳ Activation functions ↳ Pooling ↳ Dropout ↳ Normalization ↳ Attention 3️⃣ Architectures ↳ MLPs ↳ CNNs ↳ Attention 4️⃣ Applications ↳ Image classification ↳ Object detection ↳ Speech recognition ↳ Reinforcement learning 5️⃣ The Compute Schism ↳ Prompt engineering ↳ Quantization ↳ Adapters ↳ Model merging --- Book by François Fleuret

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