The disconnect between LLM theory and job reality.

Day 1: Feature Engineering Journey 🚀 Today, I’ve started learning Feature Engineering — an essential step in the Data Science and Machine Learning process. 💡 What I explored today: What exactly is Feature Engineering? Different types of Feature Engineering Simple definitions and practical examples Feature Engineering feels like turning raw data into gold — and I’m just getting started! ✨ 👉 If you’re a Data Scientist or ML enthusiast, what’s one tip you’d give a beginner about Feature Engineering? 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 #DataScience #MachineLearning #LearningJourney #AI

🚀 " Your Fast-Track ML Roadmap "🚀 Here’s the high-level path we’ll walk together in this series 🧠 📦 Module 1: ML Pipeline & Data Prep -Cleaning, scaling, feature engineering -Exploratory Data Analysis (EDA) -Model evaluation, cross-validation, tuning 📊 Module 2: Supervised Learning -Regression, Classification -Decision Trees, SVM, KNN, Naïve Bayes -Ensembles & boosting 🔍 Module 3: Unsupervised Learning -Clustering (KMeans, DBSCAN) -Dimensionality reduction (PCA, t-SNE) -Association rules 🤖 Module 4+: Advanced & Deployment -Reinforcement Learning basics -Semi-supervised & forecasting models -Model deployment, APIs, MLOps 👉 Why follow this path? -Builds from fundamentals to advanced -Covers both theory and production skills -Prepares you for real-world ML roles Let’s start strong — in upcoming days, I’ll deep dive into each topic, one concept at a time. Stay tuned! #MachineLearning #MLRoadmap #DataScience #LearnWithMe #MLBeginner

🚀 AlgoBench: Machine Learning Algorithm Performance Across Dataset Sizes — Dataset Now Published! I’m excited to share the benchmark dataset I have compiled and published 👉 https://lnkd.in/gKzi4Fvh . This work was carried out and would not be so presentable without the guidance of Dr. Deepali Pankaj Javale mam. 📊 What’s inside the dataset Instances: 4,036 Features: 16 (mix of categorical & numerical) Format: CSV (<1 MB) — ready for direct use in ML pipelines Purpose: Training, evaluation & baseline comparisons across diverse ML classifiers. 🧪 Benchmarking Setup To create robust baselines, I evaluated 8–9 large classification datasets by applying five core algorithms on different sample sizes with systematically varied parameters: • Logistic Regression (LR) – tuned the regularization strength parameter C, starting at 1.0 and increasing by 0.5 each step. A larger C reduces regularization, allowing the model to fit the training data more closely. • Decision Tree (DT) – adjusted the tree’s max_depth, beginning at 5 and increasing by 2 per iteration. Deeper trees capture more complex patterns but risk overfitting, while shallower trees generalize better. • K-Nearest Neighbors (KNN) – varied the number of neighbors n_neighbors (k), starting from 3 and increasing by 2. A small k can make the model sensitive to noise; a larger k smooths predictions. • Random Forest (RF) – increased the number of trees n_estimators, starting at 100 and adding 50 at each step. More trees generally improve performance but raise computational cost. • Naive Bayes (NB) – kept the var_smoothing parameter fixed at 1e-9, which ensures numerical stability in probability calculations. • XGBoost (XGB) – grew the number of boosting rounds n_estimators, starting at 100 and adding 50 per iteration to enhance ensemble strength. I’d love for the ML & Data Science community to explore the dataset, run your own experiments, and share your insights! 🔗 https://lnkd.in/gKzi4Fvh #MachineLearning #Benchmarking #OpenData #DataScience #MLResearch #AI #XGBoost #RandomForest #LogisticRegression #KNN #DecisionTree #NaiveBayes

What does it take to succeed in data science? Strong coding and AI/ML knowledge are essential — but they’re just the beginning. Check out my talk at the next MLOps Denver meetup where I'll share five key lessons from over eight years in the field. From advocating for yourself to building cross-functional collaboration, this talk goes beyond the technical to explore what truly drives long-term success in a technical field. Hope to see you there! #DataScience #CareerGrowth #AI #MachineLearning #WomenInTech #ProfessionalDevelopment

Ready to speed up your data science workflow? Google’s new Data Science Agent in Colab Enterprise is here to help! This AI-powered assistant automates exploratory analysis, data cleaning, feature engineering, modeling, and visualization: all with natural language prompts. How to get started: - Open Colab Enterprise and activate the Data Science Agent. - Ask questions like "Analyze this dataset” or “Visualize missing values.” - Get instant suggestions, code, and clear visualizations. Benefits for data pros: • Accelerate messy data cleaning and feature selection • Rapidly test and tweak ML models • Visualize insights with simple commands • Save more time for impactful work Example: Quickly generate EDA reports, build features, or fine-tune classification models: all with a few clicks or prompts! Explore more and see official docs: https://lnkd.in/gCc-rJW2 At Dallas Data Science Academy, we teach students to leverage tools like this to get job-ready. Start your AI career with Dallas Data Science Academy! #DataScience #AI #EdTech #CareerGrowth #Bootcamp

⚠️ “Garbage in → Garbage out. But with AI? It’s even worse.” Bad data collapses models. Quality checks keep them alive. 🔹 Why it matters: Prevents misdiagnosis in healthcare AI Keeps fraud alerts accurate Ensures consistent model performance 💡 Data Engineers = Guardians of AI truth. 👉 Master data quality: https://zurl.co/t7ChU 📌 A follow helps me create more deep-dives. 👥 Free community of 100+ engineers: Python + SQL + PySpark learning + referrals 👉 https://zurl.co/ddDzz

⚠️ “Garbage in → Garbage out. But with AI? It’s even worse.” Bad data collapses models. Quality checks keep them alive. 🔹 Why it matters: Prevents misdiagnosis in healthcare AI Keeps fraud alerts accurate Ensures consistent model performance 💡 Data Engineers = Guardians of AI truth. 👉 Master data quality: https://zurl.co/t7ChU 📌 A follow helps me create more deep-dives. 👥 Free community of 100+ engineers: Python + SQL + PySpark learning + referrals 👉 https://zurl.co/ddDzz

 Just wrapped up a hands-on demo exploring how to fine-tune LLMs for tabular data — and it’s been a fascinating ride! We started with a classic MLP baseline on a Kaggle competition dataset, then pushed boundaries by adapting LLMs to structured data. Two approaches stood out: 🔹 Formatting tabular rows as natural language prompts 🔹 Feeding embedded features directly into a custom transformer Custom transformer treats each feature as a token — unlocking attention across columns. This gave us powerful insights into how different features interact, layer by layer, and how attention shifts for positive vs negative class samples. 💡 Bonus: Visualized attention maps to interpret model behavior and feature influence — a step toward more transparent AI in tabular domains. 📘 Repo includes: End-to-end notebook: preprocessing, training, fine-tuning, visualization Custom transformer architecture Tools to extract and plot attention maps 🔗https://lnkd.in/d9UKZDEQ If you’re working with tabular data and curious about LLMs, interpretability, or attention-based modeling — let’s connect! Always happy to exchange ideas and learn together. #LLM #TabularData #Transformers #AttentionMaps #MachineLearning #Kaggle #AIInterpretability #DeepLearning #MLResearch

Another incredible deep dive into Generative AI applied to Data Engineering, part of Databricks Week 2.0 with Luan Moreno Medeiros Maciel and the Engenharia de Dados Academy. Hands-on demonstration of how fast the integration between data, ML, and natural language is evolving. It clearly signals a transition into the AI Data Engineer era. Key Takeaways on the Databricks Platform: - Prompt Engineering & Experimentation: Deep dive into prompt design, AI Functions, and hands-on use of the AI Playground. - Simplified GenAI & RAG: Practical implementation of GenAI, RAG (Retrieval-Augmented Generation), and intelligent agents using the Model Context Protocol (MCP). - Scalable ML/AI Ops: Leveraging MLflow for GenAI project tracking and setting up scalable AI Service Endpoints and AI Gateways. - Advanced Automation: Exploring Agent Bricks and the architecture of Multi-Agent Systems and Chains of Agents. The speed and simplicity with which these advanced concepts migrate from theory to daily engineering practice are truly mind-blowing. The power of Databricks' All-in-One Engine is undeniable. Special thanks to Torey (Markowitz) Bublitz for the energy that made this entire learning experience possible. #GenerativeAI #Databricks #DataEngineering #AIEngineer #MCP #RAG #MLOps

Is complex code the only path to mastering machine learning? Or can 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 lie in leveraging 𝐬𝐢𝐦𝐩𝐥𝐢𝐜𝐢𝐭𝐲 ? Through this course “𝐁𝐮𝐢𝐥𝐝 𝐘𝐨𝐮𝐫 𝐅𝐢𝐫𝐬𝐭 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐔𝐬𝐢𝐧𝐠 𝐃𝐚𝐭𝐚𝐢𝐤𝐮 ” I explored this question by constructing a full ML pipeline with zero coding. Using AutoML on a debt prediction dataset, the model achieved over 90% prediction accuracy. The experience underscored how the future of data science is not only about algorithms, but also about accessible platforms, efficient design, and intelligent use of tools to convert data into insight. #Data #ML #FutureOfWork #AutoML