mohammad majidi’s Post

Day 2: Feature Engineering Journey 🚀 Today, I continued my exploration of Feature Engineering with a focus on Feature Scaling — a vital step in preparing data for machine learning models. 💡 What I explored today: Understanding Feature Scaling and its importance. The concept of Standardization, a key type of Feature Scaling. The effects of Standardization on data and model performance. When Standardization is essential in Machine Learning. Feature Scaling might seem simple, but it plays a huge role in ensuring that models learn efficiently and perform accurately. 👉 If you’re a Data Scientist or ML enthusiast, what’s one tip you’d give a beginner about Feature Scaling? Drop your thoughts or favorite learning resources in the comments — I’d love to hear from you! 🙌 I’ve also created a note of today’s learning to document my progress. #FeatureEngineering #FeatureScaling #DataScience #MachineLearning #LearningJourney #AI

Want to build real-world machine learning solutions, not just models? "Machine Learning Systems" is an open-source textbook from Harvard that focuses on engineering the entire ML lifecycle … from data pipelines and system design to deployment and monitoring. The book offers hands-on labs, practical examples, and guidance for students, educators, and engineers. It’s a great resource if you want to actually create robust and scalable AI systems, not just experiment in notebooks. Highly recommended for anyone serious about applied machine learning. PDF link in the comments! #MachineLearning #MLOps #EdgeAI #OpenSource #AIcommunity

In the world of machine learning, it’s often not enough to build models that predict, we also need models that explain. I’ve been looking into the work of Sercan Arık and Tomas Pfister, whose paper “TabNet: Attentive Interpretable Tabular Learning” introduces a concept that’s highly relevant to our predictive‑analytics efforts. Their key innovation is the sequential attention mechanism for tabular data: instead of treating all input features as equally relevant for every example, the model dynamically decides, at each decision step, which subset of features to “attend” to. The architecture uses sparse masks (via sparsemax) to select features, then transforms and aggregates them through a series of decision‑steps. Why this matters: Efficiency & focus: By attending only to the most relevant features at each step, the model uses its capacity more effectively (instead of spreading across many weak signals). Instance‑wise interpretability: Each input has its own attention mask, so you can track which features drove that particular prediction. This gives you local explanations. Global interpretability: Across all decision‑steps and inputs, you can aggregate masks to see which features matter overall for the model. Handling nonlinear, multivariate interactions: Because decision steps build upon attended features, the model can capture complex dependencies (e.g., feature A matters only if feature B was selected in a prior step). In my team's project, where we’re building a winner‑predictor engine for F1 races, this concept is extremely useful: We deal with tabular data: driver‑history stats, constructor performance, track features, qualifying times, weather conditions. We expect conditional feature importance: e.g., driver past performance matters only if team strategy is optimal, track layout matters only if qualifying position is good, etc. A sequential attention model can adapt which features to consider in which context. We want explanations, not just predictions: With attention masks we can trace why the model predicted a given driver as likely to win, which builds trust and insight. So a big thanks to Sercan Arık & Tomas Pfister, their work is helping us frame our model not just as a prediction engine, but as a reasoning engine. https://lnkd.in/e_WHPgXR #MachineLearning #InterpretableAI #TabularData #FeatureAttention #DataScience #PredictiveModeling

Day 4: Feature Engineering Journey 🚀 Today, I continued my exploration of Feature Engineering with a focus on Feature Encoding — a vital step in preparing data for machine learning models. 💡 What I explored today: Difference between Standardization and Normalization. Encoding defination and types. What is Ordinal Encoding and Label Encoding? Feature encoding might seem simple, but it plays a huge role in ensuring that models learn efficiently and perform accurately. This is very important because machine learning model understand number not string. 👉 If you’re a Data Scientist or ML enthusiast, what’s one tip you’d give a beginner about Feature Encoding? Drop your thoughts or favorite learning resources in the comments — I’d love to hear from you! 🙌 I’ve also created a note of today’s learning to document my progress. #FeatureEngineering #FeatureEncoding #DataScience #MachineLearning #LearningJourney #AI

Just came across this excellent book "Machine Learning Systems" by Prof. Vijay Janapa Reddi (Harvard University). It focuses on the systems side of AI — data pipelines, training frameworks, optimization, deployment, and responsible ML. Great for anyone learning how real ML systems are built end-to-end. Highly recommended for ML/AI enthusiasts and engineers! [Link is in the comments]

Now Live: The Path to AI Expertise — Step 3: Machine Learning Fundamentals https://lnkd.in/duMUgHMK All books are free to read and download — because AI education should be open to everyone. This book takes you into the heart of Artificial Intelligence — where data becomes intelligence. Inside you’ll explore: Supervised, Unsupervised & Reinforcement Learning Model training, optimization & data pipelines Real-world ML case studies And the bridge from classical ML to modern deep learning Learn how to think like a machine learning engineer — from math to models to mastery. Catch up on the first two books in the series: Step 1 — Data Structures → https://lnkd.in/dZ6cctr9 Step 2 — Algorithms & Problem-Solving → https://lnkd.in/dQHGHn_z Step 3 — Machine Learning Fundamentals → https://lnkd.in/duMUgHMK Over the past months, I’ve been building The Path to AI Expertise — a 10-part open-learning journey designed to make AI accessible, structured, and deeply intuitive. The Complete Roadmap 1. Data Structures 2. Algorithms & Problem-Solving 3. Machine Learning Fundamentals 4. Deep Learning & Neural Architectures 5. Training & Optimization 6. Deployment & MLOps 7. Responsible & Generative AI 8. Multi-Agent Systems 9. AI Governance & Alignment 10. Collective Intelligence & the Post-AGI Era If you’ve found this series helpful, please consider sharing it so more learners can begin their journey. I’d love to hear what topics you’d like explored in the upcoming steps — your feedback helps shape every next book. #ArtificialIntelligence #MachineLearning #DataScience #DeepLearning #AIeducation #LearningJourney #AIforEveryone #Technology #CareerGrowth #ThePathToAIExpertise #AnirbanDutta

Day 3: Feature Engineering Journey 🚀 Today, I continued my exploration of Feature Engineering with a focus on Feature Scaling — a vital step in preparing data for machine learning models. 💡 What I explored today: What exactly is Normalization? The effects of Normalization on data and model performance. Different types of Normalization Simple definitions and practical examples When Normalization is essential in Machine Learning? Feature Scaling might seem simple, but it plays a huge role in ensuring that models learn efficiently and perform accurately. 👉 If you’re a Data Scientist or ML enthusiast, what’s one tip you’d give a beginner about Feature Scaling? Drop your thoughts or favorite learning resources in the comments — I’d love to hear from you! 🙌 I’ve also created a note of today’s learning to document my progress. #FeatureEngineering #FeatureScaling #DataScience #MachineLearning #LearningJourney #AI

📚Book Review: Reliable Machine Learning — Applying SRE Principles to ML in Production As someone working on stable and scalable AI systems, I found this book an absolute must-read. It bridges the gap between model accuracy and system consistency, showing how to make ML truly work in production — not just in theory but also in production scenario, overall highly practical book. What stood out for me: ⚙️ Defines the ML lifecycle as a production process 📊 Treats data as a versioned, valuable asset 🧠 Explains training & serving architecture 🛠️ Covers drift monitoring and issue handling 🏢 Shares strategy for AI team and org design It’s a clear, practical guide for anyone scaling ML systems with confidence. 📄 Want the PDF? DM me — happy to share! #MachineLearning #MLOps #AI #SystemDesign #DataScience #AIOps

🔍 What if the next frontier of AI isn’t just “bigger” models — but smarter learning systems? I just came across the research paper “Nested Learning: The Illusion of Deep Learning Architectures” from Google Research and it really challenges how we think about model design. • Nested update schedules – instead of just stacking layers, this paradigm treats each component (weights, optimizer, memory) as an “update loop” running at its own speed. • Optimizer as memory module – the paper argues that optimizers like Momentum or Adam are themselves associative memory systems compressing context flows. • Continuum memory system – rather than only “short-term” vs “long-term” memory, the authors propose a spectrum of memory modules each updated at different frequencies. How might you rethink your next model architecture if you treated memory, optimizer and parameters as distinct “time-scale” learners? #ArtificialIntelligence #MachineLearning #AIEngineering #ModelArchitecture #ContinualLearning #AdaptiveAI #TechInnovation #OpenSourceAI

🚀 Ready to demystify Machine Learning? 🚀 In my latest deep-dive, I break down the TWO fundamental approaches powering today’s AI revolution: • Supervised Learning (Regression & Classification) • Unsupervised Learning (Clustering) Why this matters: • 🎯 Supervised models forecast future trends—think stock prices, real-estate valuations, or spam detection. • 🔍 Unsupervised algorithms uncover hidden patterns—powering customer segmentation, anomaly detection, and more. Key highlights: • 00:38 – What is Machine Learning? • 1:54 – Supervised Learning in action • 5:25 – Unsupervised Learning explained • 7:05 – Real-world examples & recap 👇 Take your AI game to the next level: Watch the full breakdown here: https://lnkd.in/gu-38keE Comment: Which ML type have you used most in your projects? Like & Share to spread the knowledge! #MachineLearning #SupervisedLearning #UnsupervisedLearning #Regression #Classification #Clustering #DataScience #AI #BigData #Tech #Innovation #AI900 #Microsoft #Azure