Level up Your ML Game ❤️️| Writing a Lot | Data Mentor

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Hi there👋

🔹 I help organizations turn data into scale products, bridging my background as a Data Scientist and AI Engineer with my current role as Chief Product Officer. I focus on data Product Strategy, transforming raw data into trusted, usable, and monetizable products.

🔹 With 7+ years leading enterprise data science teams (Allianz, startups) and now building SaaS at ARIF, I’ve seen how models, dashboards, and pipelines fail without a product mindset. That’s why I work on frameworks that move teams from data as an asset → data as a product.

Some highlights from my journey:
✅ Propensity models that drove 150% of Allianz’s annual revenue targets
✅ Fraud detection systems reducing losses by 7-figure margins
✅ LLM-powered chatbots with RAG cutting customer support costs by 30%
✅ SaaS data platform (ARIF) turning advanced analytics into a productized experience

🔹 I also share what I learn. I’ve written 500+ articles (KDnuggets, Towards Data Science, MachineLearningMastery, Non-Brand Data) for 2M+ monthly readers, translating complex AI/ML + data strategy concepts into actionable playbooks.

🔹 Today, I collaborate with professionals and organizations on:
👉 Consulting: Data product strategy, machine learning pipelines, and LLM product design
👉 Content & Speaking: Articles, workshops, and keynotes on Data Science and AI

📌 If you want to bridge the gap between data teams and business outcomes. Let’s talk.
📩 Write to me for consulting, speaking, or content partnerships.

The internet is casually giving away the best ML books ever written.

No gatekeeping. No affiliate links. Just the stuff your future self wishes you read earlier.

Here’s the FREE ML bookshelf I keep coming back to:
1) The classics
👉 An Introduction to Statistical Learning (ISLR/ISLP): https://lnkd.in/dwggK-zu
👉 The Elements of Statistical Learning (ESL) (free PDF): https://lnkd.in/dk-ENXZr

2) The math for ML
👉 Mathematics for Machine Learning (free PDF): https://lnkd.in/dr47yYen
👉 Convex Optimization (Boyd & Vandenberghe) (free PDF): https://lnkd.in/dkADaY7p

3) Theory, but in a “now I get it” way
👉 Understanding Machine Learning: From Theory to Algorithms (free PDF): https://lnkd.in/dH8virs4
👉 A Course in Machine Learning (free PDF download): https://ciml.info/

4) Deep learning 
👉 Deep Learning (Goodfellow, Bengio, Courville) (free online edition): https://lnkd.in/djFj-j8x
👉 Dive into Deep Learning (D2L) (free interactive book): https://d2l.ai/

5) Probabilistic ML
👉 Probabilistic Machine Learning: An Introduction (free draft PDF linked): https://lnkd.in/dVJ9D5A2
👉 Probabilistic Machine Learning: Advanced Topics (free draft PDF linked): https://lnkd.in/dYJyd2fB
👉 Gaussian Processes for Machine Learning (Open Access): https://lnkd.in/daPcyRTf

6) The part everyone ignores
👉 Machine Learning Systems (free online + PDF download link): https://mlsysbook.ai/

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Data Scientist & AI Engineer | Data Insight | Helping Orgs Scale with Data

Gobi  Durairaj

SQL is still one of the most valuable skills you can learn.

Not because it looks impressive.

Because it makes you useful.

If you can write SQL, you can answer questions yourself:
→ What changed?
→ Where is revenue leaking?
→ Which users are dropping off?
→ What is actually happening in the data?

That skill compounds fast.

SQL helps you think clearly, move faster, and rely less on dashboards, screenshots, or waiting on someone else to pull numbers.

And the barrier to entry is low.

𝐘𝐨𝐮 𝐜𝐚𝐧 𝐬𝐭𝐚𝐫𝐭 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐅𝐑𝐄𝐄:
👉 SQLBolt: https://sqlbolt.com/
👉 Khan Academy — Intro to SQL: https://lnkd.in/gD4Gj66g
👉 SQLZoo: https://lnkd.in/gVWbsEjq
👉 CS50’s Introduction to Databases with SQL: https://lnkd.in/gbCfQZ-5
👉 Microsoft Learn — Introduction to Transact-SQL: https://lnkd.in/gUTPNT36
👉 Microsoft Learn — Query and modify data with Transact-SQL: https://lnkd.in/gj_p8hpS
👉 Oracle FreeSQL: https://lnkd.in/g7P28U-t
👉 Oracle Dev Gym — Databases for Developers: Foundations: https://lnkd.in/gHazfpZH
👉 PostgreSQL Tutorial: https://lnkd.in/gVvQrzQU
👉 SQLite Documentation: https://lnkd.in/gwGpTUTV
👉 MySQL Tutorial: https://lnkd.in/girTn6Mm
👉 W3Schools SQL Tutorial: https://lnkd.in/gziqnvh3
👉 freeCodeCamp — The SQL Handbook: https://lnkd.in/gu6yM9Rf
👉 HackerRank SQL: https://lnkd.in/gbb2Jdi4
👉 LeetCode SQL 50: https://lnkd.in/g86Cpgvt
👉 Use The Index, Luke: https://lnkd.in/gmwBQ4nU
👉 ThoughtSpot SQL Tutorial: https://lnkd.in/gv9UEx4G

What’s the first thing SQL helped you understand better?

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Many people believe they are not skilled in SQL.

Usually, they are not.

They are just learning it in the wrong order.

Most SQL resources teach syntax first:
SELECT
FROM
WHERE
GROUP BY
joins

But real work is rarely about memorizing clauses.

It is about recurring business use cases:
📌 KPI reporting
📌 funnel analysis
📌 cohort retention
📌 segmentation
📌 experiment analysis
📌 data quality checks
📌 operational monitoring

That is why I wrote this piece:
7 𝐒𝐐𝐋 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 𝐄𝐯𝐞𝐫𝐲 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥 𝐒𝐡𝐨𝐮𝐥𝐝 𝐊𝐧𝐨𝐰

The goal is to connect SQL to actual work.

🔥Read it here: https://lnkd.in/gUyqTJC3

To help you learn further, here are 𝐅𝐑𝐄𝐄 resources for actual SQL use cases:

👉 Investigating a Drop in User Engagement (free SQL case study): https://lnkd.in/gKrZE_6f
👉 Understanding Search Functionality (free SQL case study): https://lnkd.in/gQUFxBWx
👉 Validating A/B Test Results (free SQL case study): https://lnkd.in/gFcTDRk3
👉 SQL Analytics Training (free real-world SQL cases): https://lnkd.in/gGaVfsvP
👉 Instacart SQL Data Analytics Case Study (free case study): https://lnkd.in/gyW5xedr
👉 Here's Why You Should Use SQL for Sales Analytics (free article): https://lnkd.in/gvSbXPn7
👉 Why You Should Use SQL in Marketing Analytics (free article): https://lnkd.in/gNA3AAf3
👉 Build a Product Inventory Dashboard With SQL (free article): https://lnkd.in/gWV_eJW7
👉 6 Advanced SQL Queries for Analyzing Financial Data (free article): https://lnkd.in/gHU5-SGy
👉 SQL for Financial Reporting (free article): https://lnkd.in/gryAZNNH
👉 How SQL Can Help You Understand Your Clients Better (free article): https://lnkd.in/gdUDAE8t
👉 How SQL Data Analysis Can Help You Understand Seasonality in Business (free article): https://lnkd.in/ggu5zVE6
👉 Building Analytical Data Pipelines with SQL (free article): https://lnkd.in/g5ejR3eA
👉 SQL Reporting Guide for Business Professionals (free guide): https://lnkd.in/ghPYZwyW
👉 SQL for Data Analysis: Tutorial Introduction (free tutorial): https://lnkd.in/gX8DcTD5

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Prompt engineering itu bukan “ilmu dukun AI”.

Itu skill komunikasi. Bedanya output “meh” vs “wow” sering cuma 3 baris instruksi yang tepat.

Dan ya, ini bisa dipelajari.

Saya akan bawain webinar GRATIS soal Prompt Engineering.

Dari prompt sederhana sampai advanced untuk kebutuhan kerja dan bisnis nyata.

Yang akan dibahas:
Strategi prompt dari zero-shot sampai tingkat lanjut
Penalaran terstruktur dan alur pengambilan keputusan
Desain prompt yang siap dipakai di dunia nyata
Penerapan LLM di kasus nyata (ChatGPT dan Gemini)

📅 28 Februari 2026
 🕓 16:00
 📍 Google Meet
 🎙️ Cornelius Yudha Wijaya (CPO ARIF, Data Scientist. 7+ tahun industri, ex-Allianz. Built real-world LLM products)

Kalau mau ikut, ketik “SIAP”. Nanti saya kirim link pendaftarannya.

Pertanyaan yang paling sering kamu pengen tanya soal prompt?

Yang bikin kamu kesel: “Kenapa modelnya salah nangkep mulu?” 😄

I still can’t believe this is free.

Multi-agent systems used to feel like “PhD-only territory”.

Now you can learn it with open courses and hands-on projects.

Here’s why multi-agent AI is worth your time:

1. Single-agent: optimize one brain.
2. Multi-agent: optimize a whole system.

And systems do weird things:
📌coordination failures
📌collaboration that looks like strategy
📌emergence that nobody explicitly programmed

That's the system we need: Observe → Think → Act → Learn
Running across multiple agents inside the same environment.

Want a FREE learning stack? Here you go:
👉 Hugging Face AI Agents Course (free + free certificate): https://lnkd.in/gSiP-ZXT

👉 Hugging Face Deep Reinforcement Learning Course (free + open-source + free certificate): https://lnkd.in/gGK7BjSa

👉 NPTEL/SWAYAM Introduction to Multi-Agent Systems (free to learn; optional paid proctored exam for certificate): https://lnkd.in/grweTum7

👉 UC Berkeley CS188 Projects (multi-agent search + RL agents, Pacman projects): https://lnkd.in/gmKVf6ZV

👉 MIT 6.S890 Topics in Multiagent Learning (open course site): https://lnkd.in/g2BRJKJc

👉 Complexity Explorer Agent-Based Models with Python (Mesa) tutorial (free): https://lnkd.in/gHvNmE-P

👉 CTU Prague Multiagent Systems (open materials): https://lnkd.in/gk3d3Bqn

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If you want more trustworthy GenAI, learn RAG.

It’s not about making the model “smarter.”

It’s about giving it the right information at the right time.
𝐑𝐀𝐆 = 𝐫𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 + 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞.

Most effort goes into the data layer, not the prompt.

If you want to start today (free), here’s a clean learning path:

1) Build a simple end-to-end RAG
🚀 Simple RAG Implementation: https://lnkd.in/gR4qHSmg

2) Learn the basics fast
📌 Introduction to RAG (Great Learning): https://lnkd.in/g2_S2AGw

📌 RAG Course for Beginners (Simplilearn SkillUp): https://lnkd.in/gDmnSAgF

📌 Intro to RAG (Coursera Project): https://lnkd.in/d-TMR2kf

3) Go deeper (production + advanced)
📌 RAG from DeepLearning.AI: https://lnkd.in/gR7jC3NA

📌 RAG for Production (LangChain + LlamaIndex): https://lnkd.in/gE_bf2TB

📌 Building & Evaluating Advanced RAG Apps: https://lnkd.in/gGNAU8xZ
4) Agentic + multimodal (if you’re feeling spicy)
📌 Building Agentic RAG with LlamaIndex: https://lnkd.in/gxi2Kj97

📌 Multimodal RAG using Vertex AI: https://lnkd.in/gzjXeXYa

📌 IBM RAG + Agentic AI Professional Certificate: https://lnkd.in/gDkHipWE

Hot take:
If your RAG doesn’t have evals, you don’t have RAG. You have vibes.

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