Skills For Digital Economy

Once, we built a machine learning model that was expected to drive a 15% lift in conversions. The result? A shocking 0.01%. What went wrong? The model worked perfectly, but the business process behind it was too long and complex. By the time the offer reached the clients, most leads were lost. And the kicker? The business case was literally giving money to the clients! This experience taught us a crucial lesson: even the best machine learning model can fail without an aligned, efficient business process. The model had identified high-value leads, but the operational workflow to turn those leads into conversions was cumbersome and slow. It involved multiple handoffs, redundant steps, and delays that made it nearly impossible for the offer to reach the client in time. In this case, the problem wasn’t technical—it was systemic. The gap between predictive insights and actionable outcomes created friction that nullified the model's value. When we revisited the process, we streamlined the journey from the model’s output to client interaction. By reducing the time and steps involved, we saw significant improvements—not just in conversion rates but also in the trust clients placed in the business. This is why aligning AI models with business operations is just as critical as building accurate models. Are your machine learning projects driving real business impact, or are they stuck in the pipeline? Let’s discuss strategies to close the gap and unlock the full potential of your AI investments. Share your thoughts or experiences below!

Machine Learning in Preclinical Drug Discovery 🧬💊 Machine learning (ML) is increasingly integrated into preclinical drug discovery, offering promising advancements across hit identification, mechanism-of-action elucidation, and translational investigations. A recent paper in Nature Chemical Biology, "Machine Learning in Preclinical Drug Discovery", provides a thorough analysis of how ML is being utilized to enhance efficiency in early-stage drug development. 🔬 Key Insights from the Paper 1️⃣ Hit Identification & Virtual Screening Traditionally, high-throughput screening (HTS) has been the gold standard for identifying potential drug candidates. However, it is resource-intensive and slow. ML-based virtual screening, powered by deep learning models and molecular featurization techniques, is enabling rapid exploration of chemical libraries far beyond what traditional HTS can achieve. The paper highlights the impact of message-passing neural networks (MPNNs) and Deep Docking as effective methods for prioritizing hit compounds. 2️⃣ Mechanism-of-Action (MOA) Elucidation Understanding how a compound interacts with biological targets is critical for drug development. ML is now playing a pivotal role in MOA elucidation through: AlphaFold and RoseTTAFold: AI-driven protein structure prediction is accelerating target identification and binding site analysis. Generative models: Variational autoencoders (VAEs) and diffusion models are not only aiding in de novo drug design but also helping predict chemical interactions with biological systems. 3️⃣ Translational Investigations & ADMET Predictions Many promising compounds fail in later stages due to poor pharmacokinetics and toxicity profiles. ML is being leveraged to enhance ADMET predictions, improving the likelihood of clinical success. The paper discusses advancements in: Solubility and Lipophilicity Predictions: ML-driven models now outperform traditional log(P) estimations, increasing the reliability of early-stage compound selection. Toxicity Screening: AI-powered tools are improving predictions of hERG binding and organ toxicity, reducing late-stage failures. 🚀 The Future of AI in Drug Discovery While ML is proving to be a game-changer, challenges remain, including data quality, interpretability of AI models, and integration with experimental validation. The paper underscores the importance of open-source datasets, AI transparency, and active learning strategies to enhance model accuracy. 🔗 Read the full paper here: https://lnkd.in/gMtXHrHi AI is reshaping the landscape of drug discovery. As these technologies evolve, collaboration between computational scientists, biologists, and chemists will be critical to unlocking their full potential. #AI #MachineLearning #DrugDiscovery #Pharma #Biotech #ArtificialIntelligence #ComputationalBiology #NatureChemicalBiology

This paper explores how machine learning can revolutionize precision medicine by enhancing diagnostic accuracy, patient stratification, prognosis, and treatment monitoring. 1️⃣ ML algorithms, particularly supervised learning models, improve diagnosis accuracy by processing vast datasets, identifying patterns, and making probabilistic decisions. 2️⃣ Unsupervised learning models help in grouping patients based on shared characteristics, enhancing personalized treatment plans. 3️⃣ ML integrates with mass spectrometry to improve metabolite identification, leveraging techniques like convolutional neural networks and in silico spectral libraries for enhanced accuracy. 4️⃣ Combining Genome-Scale Metabolic Models with ML provides deeper insights into genotype-phenotype relationships, improving predictions and patient-specific treatments. 5️⃣ ML methods streamline the processing of complex, multi-dimensional data from various sources, overcoming traditional computational challenges. 6️⃣ Effective integration of ML in clinical practice requires overcoming regulatory, organizational, and methodological hurdles, ensuring secure data handling, and aligning with clinical workflows. 7️⃣ Few ML-enabled devices are approved due to stringent regulations. Overcoming this requires robust development, validation, and adherence to regulatory standards. ✍🏻 Henning Nilius, Sofia Tsouka, Michael Nagler, Mojgan Masoodi. Machine learning applications in precision medicine: Overcoming challenges and unlocking potential. Trends in Analytical Chemistry. July 15, 2024. DOI: 10.1016/j.trac.2024.117872

🚀 𝐌𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐣𝐮𝐦𝐩 𝐢𝐧𝐭𝐨 𝐀𝐈 & 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠… 𝐁𝐮𝐭 𝐬𝐤𝐢𝐩 𝐭𝐡𝐞 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐦𝐚𝐤𝐞𝐬 𝐦𝐨𝐝𝐞𝐥𝐬 𝐰𝐨𝐫𝐤. That’s where things break. I recently explored a complete guide on Supervised Learning, and here’s what truly stood out 👇 💡 Supervised Learning = Learning with Guidance It’s not magic. It’s a structured process where models learn from labeled data (Input → Output) to make accurate predictions. Think of it like teaching a child with answers already provided. 📊 Two Core Types You Must Know • Classification → Predicts categories (Spam vs Not Spam) • Regression → Predicts numbers (House prices, salary, temperature) Simple concept. Massive real-world impact. ⚙️ What Actually Makes Models Learn Behind every prediction, there’s a system: • Hypothesis Function → Defines how input becomes output • Loss Function → Measures how wrong the model is • Gradient Descent → Improves the model step by step • Regularization → Prevents overfitting • Bias–Variance Tradeoff → Balances simplicity vs complexity Miss this, and you’re just using tools blindly. 🔁 The Real ML Workflow (Most Underrated Part) Raw Data → Clean → Split → Train → Evaluate → Tune → Deploy Sounds simple. But this pipeline decides whether your model works in real life or just in notebooks. 🤖 Popular Algorithms You Shouldn’t Ignore Linear Regression. Logistic Regression. Decision Trees. Random Forest. SVM. kNN. Gradient Boosting. Each solves a different kind of problem. There is no “one-size-fits-all” model. 📈 Metrics Matter More Than Accuracy Accuracy alone can mislead you. You need: • Precision • Recall • F1 Score • RMSE / MAE Because in real-world problems, not all errors are equal. ⚠️ Biggest Mistakes Beginners Make • Ignoring data quality • Overfitting models • Skipping validation • Choosing complexity over clarity 🎯 Final Insight Machine Learning is not about building models. It’s about building models that generalize well on unseen data. That’s the real game.

Sharing key learnings and insights from our Real-Time (In-Session) Personalization journey at CARS24 — a capability that has transformed how we personalize the car buying experience at scale. Leveraging advanced sequence-based neural networks and real-time Kafka streaming infrastructure, we've developed a dynamic machine learning pipeline that processes more than a million user interactions daily. Our deep learning models rapidly adapt to user behaviour, delivering personalized car recommendations with sub-200ms latency. Highlights: ✅ Advanced sequence-based neural network architecture  ✅ Real-time streaming and processing of user behaviour signals with Kafka  ✅ Rapid feature engineering and inference using optimized real-time databases  ✅ High scalability for continuous model retraining and deployment Performance Impact: 📈 Across all discovery widget we achieved a highest Impression-to-View (I2V) rate and on the 'Best Matches' recommendation rail on our car detail page and buyer home page. 📈 Delivered a strong Impression-to-Booking Initiation (I2BI) conversion rate across different discovery widgets, underscoring high user relevance and engagement. Business Outcomes: 🚀 Significant uplift in user engagement  🚀 Marked reduction in user drop-offs  🚀 Enhanced personalization and superior user experience The attached flow chart outlines the architecture behind this AI-powered personalization pipeline — from real-time clickstream ingestion to ML inference and personalized recommendations. #RealTimePersonalization #AI #MachineLearning #DeepLearning #Kafka #DataScience #RecommendationEngine #TechInnovation #AI  #Personalization #pubsub #CARS24 #transformers #llm #genai

Pulled the AI usage logs for a client last week. Found a prompt being run 340 times per day. Same exact prompt, same user, same terrible output. Nobody had looked at the data in six months. Here's what your AI logs reveal that transforms how you use AI in 4 insights: Insight 1: The Repetitive Task Map Your logs show which prompts run most frequently: ↳ Customer research: 87 times per day ↳ Email responses: 64 times per day ↳ Meeting summaries: 52 times per day These are automation candidates. One company found their team was manually prompting for the same sales script 40+ times daily. Built a custom GPT that generates it in one click. Saved 6 hours per week across the sales team. Your high-frequency prompts are screaming "automate me." Insight 2: The Failed Prompt Patterns Track which prompts get abandoned or regenerated multiple times: ↳ Prompt attempted ↳ Output reviewed ↳ Immediately regenerated 4-5 times ↳ User gives up These are broken workflows costing you money. One marketing team had a product description prompt with 78% regeneration rate. Fixed the prompt structure. Success rate jumped to 91% on first try. Logs tell you which prompts need urgent fixes. Insight 3: The Training Gap Identifier Compare AI usage across team members: ↳ Top performer: 23 prompts per day, 89% success rate ↳ Struggling user: 8 prompts per day, 34% success rate Same tools, wildly different results. Export the successful prompts from your top performers. Turn them into training materials for everyone else. One sales team closed the gap in three weeks by sharing their best prompts. Your logs reveal who knows how to use AI and who needs help. Insight 4: The Security Audit Trail Your logs show exactly what data entered AI systems: ↳ Who uploaded customer contracts ↳ When sensitive financial data was processed ↳ Which prompts contained internal-only information Critical for compliance audits and security reviews. One company discovered an employee had been uploading competitor contracts to ChatGPT. Legal flagged it because they audited logs monthly. Caught it before it became a lawsuit. Most teams treat AI logs like exhaust fumes. They're actually a diagnostic tool showing you exactly how to improve. Pull your logs this week. The patterns will surprise you. What are you discovering in your AI usage data? P.S. Want to learn more about AI? 1. Scroll to the top 2. Click "Visit my website" 3. Sign-up for our free newsletter

Groundbreaking Research Alert! 🚨 We're excited to share our latest preprint: *"Predicting targeted- and immunotherapeutic response outcomes in melanoma with single-cell Raman Spectroscopy and AI"* 🎉 *Breakthrough Discovery:* Our team has developed a revolutionary approach that combines Raman spectroscopy and machine learning (ML) to predict treatment outcomes in melanoma patients. This innovative method analyzes individual cells, providing unprecedented insights into the tumor microenvironment. *What did we achieve?* - *96%+ accuracy*: Our model can differentiate between various cell types and functional phenotypes in the tumor microenvironment. - *91% accuracy*: We successfully predicted resistance likelihoods for 30 out of 33 clinically relevant patient-drug combinations. *How does it work?* Our approach uses Raman spectroscopy to capture the unique biochemical signatures of individual cells. By applying machine learning algorithms, we can identify patterns and predict treatment outcomes. *Impact:* This breakthrough has the potential to transform precision medicine by enabling clinicians to make informed decisions about treatment strategies. Our scalable, prognostic model can help advance clinical biomarker efforts and improve patient outcomes. *Read the full preprint here:* (https://lnkd.in/gp6rE4ps) Stay tuned for more updates! 🔥 #MelanomaResearch #PrecisionMedicine #RamanSpectroscopy #MachineLearning #CancerResearch #NewPreprint #ScientificBreakthrough #MedicalInnovation

💡 𝐘𝐨𝐮 𝐝𝐨𝐧’𝐭 𝐫𝐞𝐚𝐥𝐥𝐲 𝐥𝐞𝐚𝐫𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐛𝐲 𝐫𝐞𝐚𝐝𝐢𝐧𝐠, 𝐲𝐨𝐮 𝐥𝐞𝐚𝐫𝐧 𝐢𝐭 𝐛𝐲 𝐬𝐨𝐥𝐯𝐢𝐧𝐠 𝐦𝐞𝐬𝐬𝐲 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬. I came across a really interesting dataset of house rent prices across Indian cities, and it turned into an opportunity to dive deep and build a full end-to-end Machine Learning project around it. From data cleaning → feature engineering → visualization → modeling → explainability, this project covered it all. Here’s what I learned (and what most beginners miss): ✅ The hardest part isn’t building the model, it’s preparing clean, usable data. ✅ EDA and feature transformations decide 70% of your model’s accuracy. ✅ You don’t need 10 algorithms - just one that fits your problem well (XGBoost, in my case). ✅ SHAP explainability adds immense value, it helps you understand why your model predicts what it does. Final Results 1️⃣ Best Model: XGBoost Regressor 2️⃣ R² Score: 0.75 on test data 3️⃣ Key Predictors: Bathrooms, Size, and Furnishing Status 💬 If you’re a fresher, data science student, or aspiring ML engineer, this project is a great way to understand how data actually flows in the real world from raw CSVs to actionable insights. I’m attaching the full project walkthrough (code + explanations) so you can explore it step by step. Because Machine Learning isn’t about fancy terms, It’s about turning data into decisions. #MachineLearning #DataScience #AI #Analytics #Python #Projects #FreshersJobs #MastersStudents #CareerSwitch #LearningPath #XGBoost #FeatureEngineering #ExplainableAI

Using time over the holidays to dive deeper into the language of AI. I started by watching the first lecture of Stanford’s CS230 (Deep Learning) course. I will link it in the comments. The first lesson is about the building blocks. AI isn’t one thing—it’s a stack. At the foundation: Computer science fundamentals: Algorithms, logic, systems, how machines actually work. The quiet backbone. On top of that: Machine learning: Models that learn patterns from data—prediction, classification, optimization. This is where “data-driven” starts to earn its keep. Then: Deep learning: Neural networks that handle scale and complexity—language, images, sequences, messy real-world problems. And finally: Generative AI: The layer everyone sees. Models that generate text, images, code, ideas—but only because everything below it exists. For those of us in marketing insights, AI is becoming the operating language of how marketing decisions get made: How signals are captured How audiences are modeled How creative is evaluated How incrementality is proven How spend is optimized in near-real time If you work in marketing, measurement, or insights and you can’t speak the basics of AI—models, training data, bias, inference, feedback loops—you’re effectively reading the subtitles while everyone else is in the conversation. This doesn’t mean everyone needs to code neural networks. It does mean: Knowing what questions to ask vendors and platforms Understanding what AI can and cannot infer from data Separating real signal from confident-sounding nonsense Translating AI outputs into decisions a CMO can actually act on For those of us in the insights business, this is especially critical. Our credibility lives or dies on interpretation. AI can scale analysis—but humans still own judgment. Yes, I used AI to help draft this post. It took several prompts, iterations, rewrites, and pushes to get it right. The structure came from AI. The wording evolved through refinement. But the point of view, judgment, and lived experience are mine. That’s actually the lesson. AI is powerful at accelerating thinking—but it still needs direction, context, and taste. The output only got better as the questions got clearer and the intent sharper. Which, in a way, reinforces the whole idea: The value isn’t in replacing human judgment. It’s in augmenting it. That’s how I’m choosing to use it—and learn it. That's the collaborative approach we're taking at Kantar.

📊 Project Highlight: Financial Forecasting with Machine Learning Regression Models During one of my data science projects, I explored how machine learning can improve financial forecasting by predicting target sales based on various economic indicators such as GDP growth, inflation rate, unemployment rate, and market trends. This project focused on understanding the relationships between macroeconomic variables and business performance using advanced regression-based approaches. 🔍 Project Overview I began by performing extensive data preprocessing using Python libraries such as pandas, NumPy, and scikit-learn. Missing values were imputed with KNNImputer, numerical features were scaled using StandardScaler, and categorical data was encoded with LabelEncoder. To understand the dataset, I performed Exploratory Data Analysis (EDA): - Generated distribution plots for major financial indicators. - Built a correlation heatmap to visualize interdependencies among features. - Conducted descriptive statistics to gain insights into market patterns. 🤖 Machine Learning Models I implemented and compared two robust regression models: - Random Forest Regressor - optimized for accuracy and interpretability, allowing me to identify which features most strongly influenced target sales. - Gradient Boosting Regressor – fine-tuned for better predictive performance using an adjusted learning rate and multiple estimators. - Both models were evaluated using metrics such as Mean Squared Error (MSE), R² Score, Explained Variance, and Max Error. - I also created visualizations comparing predicted vs actual sales to assess the models’ accuracy and interpretability. 📈 Key Insights & Outcomes * The machine learning models demonstrated that macroeconomic factors significantly influence sales trends, especially in volatile market conditions. * Through feature importance and regression outcomes, I was able to pinpoint economic indicators that consistently contributed to accurate financial forecasting. 🧠 Skills & Tools Gained - Data Cleaning and Preprocessing (KNNImputer, Label Encoding, Scaling) - Exploratory Data Analysis (pandas, seaborn, matplotlib) - Machine Learning Regression (Random Forest, Gradient Boosting) - Model Evaluation and Visualization - Financial Data Analytics and Forecasting 💬 This project strengthened my ability to combine financial domain knowledge with machine learning techniques — helping transform data into actionable insights that support better business decisions. I’m currently seeking opportunities in data science, business analytics, and financial analytics where I can continue applying these techniques to solve real-world challenges. #MachineLearning #FinancialForecasting #DataScience #Python #RegressionModels #RandomForest #GradientBoosting #EDA #FinanceData #PredictiveAnalytics #BusinessIntelligence #JobSearch #DataAnalytics