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I taught myself machine learning > 10 years ago. If I had to start again today, I wouldn’t touch models, LLMs, or agents first, as many AI experts suggest. I'd start with the math and the code. Ugly truth: 90% of people skip the foundations, then wonder why everything feels like magic or falls apart in production. If you want to be different, actually understand ML, not just copy-paste, this is the roadmap I'd follow: Start with fundamentals: Because no matter how fast LLMs or GenAI evolve, your math, code, and logic will keep you relevant. Here's what you should focus on: 📐 1. Linear Algebra Learn these core ideas: Vectors, matrices, tensors Matrix multiplication (dot products, broadcasting) Transpose, inverse, rank, determinants Eigenvalues & eigenvectors (especially for PCA & embeddings) Projections and orthogonality ✅ Use NumPy to implement everything yourself → Practice matrix ops, dot products, and visualizing transformations with Matplotlib 🔁 2. Calculus Focus on: Derivatives & partial derivatives Chain rule (for backpropagation in neural nets) Gradient descent Convex functions, minima/maxima ✅ Use SymPy or JAX to visualize and compute derivatives → Plot functions and their gradients to develop deep intuition 🎲 3. Probability You need a solid grip on: Random variables (discrete & continuous) Conditional probability & Bayes' rule Joint & marginal probability The Chain rule Expectation, variance, entropy Common distributions: Bernoulli, Binomial, Gaussian, Poisson Central limit theorem The law of large numbers ✅ Simulate simple probability experiments in Python with NumPy → E.g. simulate sampling from distributions 📊 4. Statistics These are must-know topics: Descriptive stats: mean, median, mode, standard deviation Hypothesis testing: p-values, confidence intervals, t-tests Correlation vs. causation Sampling, bias, and variance Overfitting/underfitting A/B testing basics ✅ Use Pandas & SciPy to explore real datasets → Calculate descriptive stats, create histograms/box plots, run t-tests 🔧 Essential Python libraries to learn early NumPy – for vectorized math and fast array ops Pandas – for loading, cleaning, and analyzing tabular data Matplotlib / Seaborn – for plotting and visualizing distributions, relationships, and trends SymPy – for symbolic math and calculus SciPy – for stats, optimization, and numerical methods Use Jupyter Notebooks(to combine math, code, & visuals in one place) 📚 Best resources to nail the fundamentals: ✅ Machine Learning Foundations Math series (ML Foundations: Linear Algebra, Calculus, Probability, and Statistics)-series of 4 courses that I've created together with LinkedIn learning ✅ Hands-On ML with TensorFlow & Keras book by Aurélien Géron ✅ The Hundred-page Machine Learning Book by Andriy Burkov If you want to become an actual ML engineer, not just someone who watches and copies demos, start here. ♻️ Repost to help others💚

Before you spend $2,000 on an AI course, read this. A lot of people are buying expensive AI courses right now. But honestly, most of them don’t need to. Some of the best AI learning resources in the world are already free. And they’re not built by self-proclaimed AI experts online.They’re created by the companies actually building the models, chips, tools, and infrastructure behind this entire AI wave. If you want to learn AI properly, it makes sense to learn directly from the source. But before that, Check out 100+ such resources shared in this community of 200K+ AI/ML Engineers: https://lnkd.in/e64Jvdrt Here's a list of 10 free AI learning platforms from industry leaders: 𝟭 - 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰: https://lnkd.in/e5fK7QUA 𝟮 - 𝗚𝗼𝗼𝗴𝗹𝗲: http://grow.google/ai 𝟯 - 𝗠𝗲𝘁𝗮: https://lnkd.in/et6wz-ta 𝟰 - 𝗡𝗩𝗜𝗗𝗜𝗔: https://lnkd.in/e8aHmFxc (GOATed) 𝟱 - 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁: https://lnkd.in/ej85NeZc 𝟲 - 𝗢𝗽𝗲𝗻𝗔𝗜: http://academy.openai.com 𝟳 - 𝗜𝗕𝗠: http://skillsbuild.org 𝟴 - 𝗔𝗪𝗦: http://skillbuilder.aws 𝟵 - 𝗗𝗲𝗲𝗽𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝗔𝗜: http://deeplearning.ai 𝟭𝟬 - 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲: https://lnkd.in/eajVCrvv If you’re serious about learning AI, the biggest difference usually comes from consistency, not another course. A simple system works surprisingly well: • Pick one learning path • Spend 30–45 minutes a day on it • Build small projects as you go • Share what you learn publicly • Repeat for 90 days That alone puts you ahead of most people who only consume content. Paid courses aren’t always bad. But too often people end up buying motivation instead of building real skills.

If you want to actually understand LLMs and Generative AI (not just copy prompts), this Stanford lecture series is still one of the cleanest, most structured, step-by-step paths I’ve found. Stanford LLM Lecture Series (watch in order): Transformer Fundamentals — https://lnkd.in/gaaRDexT Transformer Models + Practical Tricks — https://lnkd.in/gy4FUwNY Transformers → Large Language Models — https://lnkd.in/gsPiCrEU How LLMs Are Trained — https://lnkd.in/gvHJvgqP Tuning & Adaptation (fine-tuning, etc.) — https://lnkd.in/g6kgtPKR Reasoning in LLMs — https://lnkd.in/gAACSUG6 Agentic LLMs (tools, planning, workflows) — https://lnkd.in/gVm6js9z Evaluation: what “good” really means — https://lnkd.in/gJhbFQ4s Recap + What’s trending now — https://lnkd.in/g5JMNTsf To make this path more practical and well-rounded, I’d pair the Stanford theory with these free, hands-on resources: • Microsoft – Generative AI for Beginners (FREE): https://lnkd.in/dTPBrQb4 • NVIDIA Developer Program (FREE access to tools, SDKs, learning): https://lnkd.in/ejMfdtuS • Google Cloud – ML & AI Training (FREE tracks): https://lnkd.in/eUP8zNik How I’d approach it: Treat this like a mini-bootcamp. One lecture at a time. Take notes. Pause often. Implement small experiments after each session. Theory + hands-on labs + real platforms is what actually builds intuition. If you’re learning LLMs heading into 2026, this combination gives you a rock-solid foundation, from fundamentals to agents to real deployment thinking.

dbt – free Kafka – free Spark – free Airflow – free Docker – free Parquet – free VS Code – free Postgres – free Superset – free AWS Free Tier – free Even the best open-source notebooks and data viz tools? Free. With a laptop and solid Wi-Fi, you can build a lot of leverage today. Every tool you need to become a top 1% data engineer is already out there. Every concept you need to learn, schemas, pipelines, orchestration, streaming, batch, SQL optimization, it’s on GitHub, in the docs, in open courses, waiting for someone willing to break stuff and build again. Nobody is stopping you from launching your first end-to-end pipeline, from joining the DataEngineering community, from reading warehouse benchmarks, or open-source PRs. You can deploy a large-scale warehouse on a free tier, learn distributed joins and shuffles on your own laptop, practice partitioning, build data lakes, automate with Python scripts and see exactly how the world runs behind the scenes. Don’t wait for the “perfect project” or a certificate. Don’t tell yourself you need permission, or a course, or someone’s LinkedIn thread to validate your skills. The tools are there. The docs are there. The community is there. What are you waiting for? Go build.

9 beginner-friendly AI agent courses worth saving (entry + advanced) *I tried to pick courses that complement each other and cover different angles that tend to come up in real projects:  memory, workflows, enterprise use cases, multi-agent systems, and hands-on implementation. There’s still a bit of overlap on the basics, but I added a short note on each in case it helps. 𝐄𝐧𝐭𝐫𝐲 𝐥𝐞𝐯𝐞𝐥 1. 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐰𝐢𝐭𝐡 𝐌𝐨𝐧𝐠𝐨𝐃𝐁 — FREE https://lnkd.in/g8KwAcgi A practical course on building AI agents that can use retrieval tools, work with live MongoDB data, and manage both short-term and long-term memory. Especially useful if you want to understand how agents move beyond prompting into orchestration, state, and real application workflows. 2. 𝟓-𝐃𝐚𝐲 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐈𝐧𝐭𝐞𝐧𝐬𝐢𝐯𝐞 (𝐆𝐨𝐨𝐠𝐥𝐞) — FREE https://lnkd.in/gJiSruTA Short and digestible. A good way to get oriented around how agents are built, evaluated, and deployed without committing to something huge. 3. 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐚𝐧𝐝 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 (𝐈𝐁𝐌) A broader look at how agentic systems and workflows are built using modern frameworks. Useful if you want a more structured view of memory, orchestration, iteration, and agentic RAG. 4. 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬: 𝐅𝐫𝐨𝐦 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐭𝐨 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 More grounded in enterprise environments and applied development. Helpful if you want to understand how agents are built for scalable, secure, and responsible use inside companies. 5. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐢𝐧 𝐀𝐜𝐭𝐢𝐨𝐧 (𝐒𝐭𝐚𝐧𝐟𝐨𝐫𝐝) Looks beyond building and into real-world organizational use. Good for getting a sense of how agent initiatives are framed, evaluated, and applied in practice. 𝐁𝐞𝐲𝐨𝐧𝐝 𝐭𝐡𝐞 𝐛𝐚𝐬𝐢𝐜𝐬 6. 𝐒𝐞𝐥𝐟-𝐈𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 (𝐒𝐭𝐚𝐧𝐟𝐨𝐫𝐝) Focuses on agents that can iteratively improve. Gives a view into where more advanced agent design is heading. 7. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬: 𝐃𝐞𝐬𝐢𝐠𝐧 𝐚𝐧𝐝 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 (𝐎𝐱𝐟𝐨𝐫𝐝) Focused on redesigning enterprise workflows with autonomous AI agents. Useful if you want to think about agent systems as workflow design, not just model behavior. 8. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐍𝐚𝐧𝐨𝐝𝐞𝐠𝐫𝐞𝐞 (𝐔𝐝𝐚𝐜𝐢𝐭𝐲) More hands-on and build-oriented. Better for people who learn by actually making things. 9. 𝐌𝐮𝐥𝐭𝐢 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 𝐰𝐢𝐭𝐡 𝐂𝐫𝐞𝐰𝐀𝐈 (𝐃𝐞𝐞𝐩𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠.𝐀𝐈) Centered on multi-agent collaboration. Useful if you want to understand coordination rather than just single-agent behavior.

If I had to start DSA all over again from scratch… here’s exactly what I’d do. 1) Pick a Language (and stick to it) I chose C++ because it’s fast, widely used in competitive programming. If you already know Java or Python, that’s fine too. The important part? Don’t keep switching languages. But I will recommend c++ 2) Start with simple problems Begin with arrays, strings, and basic maths problems. The goal here is to get comfortable with problem statements, not to solve the hardest problems on day 1. Try platforms like HackerRank, or LeetCode Easy section. 3) Move topic by topic One topic at a time. My flow was- Arrays → Strings → Recursion → Linked List → Stack & Queue → Trees → Graphs → Dynamic Programming. Don’t jump randomly, it kills consistency. 4)Striver’s DSA Sheet ( I followed) When you’re done with basics, start Striver’s DSA Sheet and try to solve it in order. It’s a mix of easy, medium, and hard problems that cover almost everything for placements. If you get stuck, watch editorials and solutions written by other coders but don’t copy-paste without understanding. 5) Watch this video once -Fastest Way to Learn Data Structure and Algorithm by Sahil & Sara on YouTube. Trust me it’s a game changer 6) Consistency > Motivation Even 1 hour a day is better than a random 5-hour in one day in whole week. Trust me don’t give gaps. If you want the exact resources I followed (roadmaps, YouTube playlists, and my notes), drop your email in the comments, I’ll share them with you. You just need to start… and then keep going. From arrays to DP, one problem at a time

If you're feeling overwhelmed with how fast AI is evolving, you're not alone. Every day there’s a new paper, a new framework, a new agent loop, and it’s easy to feel like you’re falling behind. But the good news is that you don’t need to learn everything all at once. What you need is structure. So I put together a 10-level AI Agents Learning Roadmap that takes you from foundations to production, layering your learning in a way that’s actually doable. 💡My recommendation: spend 2–3 weeks on each level. Learn the concepts, implement small projects, and build your intuition. If you're moving faster or slower based on time or experience, that’s okay too. And when something new drops? That can be your Level 11. Don’t let “newness” derail your plan. Just start here. 👇 Here’s the roadmap: 🔖 Level 1: GenAI & Transformer Foundations Tokens, embeddings, transformers, decoding, and inference with open-weight models. 🔖 Level 2: Prompting & Language Model Behavior Prompt types (CoT, ReAct, ToT), decoding strategies, context design, and adversarial prompting. 🔖 Level 3: Retrieval-Augmented Generation (RAG) Chunking, embeddings, vector DBs, RAG pipelines, and RAG evaluation. 🔖 Level 4: LLMOps & Tools LangChain, LangGraph, Dust, CrewAI, tool use, function calling, and synthetic data. 🔖 Level 5: Agents & Agent Frameworks Agent types, memory, planning, LangChain agents, LangGraph loops, and evaluation. 🔖 Level 6: Memory, State & Orchestration Vector and symbolic memory, episodic vs persistent state, memory compression. 🔖 Level 7: Multi-Agent Systems Hub-and-spoke vs decentralized, message passing, collaborative agents, agent teams. 🔖 Level 8: Evaluation & Reinforcement Learning LLM-as-a-Judge, RLHF, RLVR, reward modeling, and self-correcting loops. 🔖 Level 9: Protocols & Safety MCP, A2A, safety alignment, guardrails, traceability, and autonomous policy updates. 🔖 Level 10: Build & Deploy FastAPI, Streamlit, GGUF, QLoRA, caching, monitoring with LangSmith, Arize, Trulens. 📌 Bookmark this. 🛠️ Build something after every level. And if you're wondering what tools to explore along the way → Start with Hugging Face (to explore LLMs and SLMs), you can use Ollama (to run SLMs on your laptop, like Phi-4, TinyLlama), or Fireworks AI (to run LLMs via endpoint, like Qwen 3, Kimi K2, DeepSeek R1), then explore LangChain & LangGraph (these two tools will teach you a lot), then you can move into learning Agentic frameworks like CrewAI, AutoGen. 💻 Pro-tip: Start with cookbooks! 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Rethinking Access: What Japan’s Mobile Study Buses Teach Us about Education in India In Japan, there is a grassroots innovation: mobile study buses that traverse neighbourhoods lacking libraries or dedicated study spaces for teens. Equipped with WiFi, books/tables/seating, and staffed by volunteer tutors, these buses offer post-school support - especially for students whose home environment may not afford quiet study or who have working parents. Meanwhile, India’s data reveals a troubling trend: according to official figures, from 2014-15 to 2023-24, the number of government schools dropped by 89,441 (an 8 % decline) while private schools increased by ~42,944. Why this matters: Schools are not just classrooms-they are access points: for mentorship, peer learning, supportive infrastructure, safe environments. A reduction in public school infrastructure can translate into lost opportunity for millions - especially in underserved zones. Claims of being a “Vishwaguru” must be matched with inclusive access and innovative outreach. What we can learn from the mobile-bus model: Deploy mobile learning hubs in areas where built infrastructure is weak. Integrate tutoring, mentoring and peer-group study into community outreach rather than rely solely on brick-and-mortar. Provide quiet spaces, connectivity and resource access to students whose home setups may not allow for focused study. Prioritise equity of access, not just numbers of institutions. For leaders in education, policy & industry: It is time to ask: Are we ensuring our youth have the spaces and resources to learn effectively? How can organisations (public/private/civic) collaborate to bring learning-on-wheels or similar outreach to underserved zones? Can we shift our metrics from “number of schools” to “effective access to learning and mentorship”? I believe: the infrastructure we build today must include mobility, flexibility, and equity. Let’s model systems not just for those who already have access-but for those who don’t. #EducationReform #SkillDevelopment #Mobility #IndiaEducation #InnovationInLearning #Leadership

Elsevier’s Researcher Academy is offering a set of FREE mini-courses that actually hit the pain points we all have: 📚 How to write a literature review 🔎 How to identify research gaps 🧪 How to conduct evidence-based research 📄 How to write an abstract & cover letter 💰 How to secure funding 🤖 Plus: Using Gen AI in research (with ethics!) These are short, practical videos (8–60 mins) you can squeeze in between experiments, clinic, or teaching. Perfect if you’re preparing a manuscript, supervising students, or just leveling up your research skills. I’m sharing this because good research isn’t just about data — it’s about presenting it so it gets published, cited, and funded. 

If I were learning SQL in 2025, Here is exactly what I would do (+ resources) 👇 I have worked as a DS in 3 different companies. I have landed DS offers from 10 different companies. The number 1 skill I’ve used on the job & in interviews? It’s SQL. Yes, I’ve used SQL more than Python as a Data Scientist. So here's how to learn SQL from scratch. 𝟭. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝗮 𝘀𝘁𝗿𝗼𝗻𝗴 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 Boring…. can’t we jump start into learning SQL? No! SQL = storing + extracting data from relational DB. So it’s really helpful to know relational databases. K͟e͟y͟ ͟c͟o͟n͟c͟e͟p͟t͟s͟ ↳ Rows vs. columns ↳ Tables vs. schemas vs. database ↳ Keys (primary, foreign & unique) ↳ Indexes ↳ Table relationships ↳ Data types: numeric, string, datetime, boolean Learn relational databases here: https://lnkd.in/gyt3q8AC 𝟮. 𝗟𝗲𝗮𝗿𝗻 𝗯𝗮𝘀𝗶𝗰 𝗦𝗤𝗟 We'll start with getting data out of a SINGLE table. F͟o͟u͟n͟d͟a͟t͟i͟o͟n͟s͟ ↳ SELECT ↳ FROM ↳ WHERE ↳ ORDER BY ↳ LIMIT ↳ AS C͟l͟e͟a͟n͟i͟n͟g͟ ͟d͟a͟t͟a͟ ↳ DISTINCT ↳ LIKE ↳ BETWEEN ↳ COALESCE ↳ CASE WHEN B͟a͟s͟i͟c͟ ͟a͟n͟a͟l͟y͟t͟i͟c͟s͟ ↳ GROUP BY ↳ HAVING ↳ COUNT ↳ SUM ↳ AVG ↳ MIN / MAX How to do analyses with SQL: https://lnkd.in/gvZjepWf 𝟯. 𝗟𝗲𝘃𝗲𝗹 𝘂𝗽 𝘆𝗼𝘂𝗿 𝗦𝗤𝗟 𝘀𝗸𝗶𝗹𝗹𝘀 C͟o͟m͟b͟i͟n͟i͟n͟g͟ ͟t͟a͟b͟l͟e͟s͟ ↳ JOINs (INNER, LEFT, RIGHT, FULL) ↳ UNION and UNION ALL ↳ CTEs vs subqueries W͟i͟n͟d͟o͟w͟ ͟f͟u͟n͟c͟t͟i͟o͟n͟s͟ ↳ OVER ↳ PARTITION BY ↳ ORDER BY ↳ ROWS BETWEEN ↳ SUM, AVG, MIN, MAX with windows ↳ RANK, ROW_NUMBER, NTILE, LAG, LEAD Intermediate SQL: https://lnkd.in/gKM9WkyA Advanced SQL: https://lnkd.in/grhDPTdK 𝟰. 𝗟𝗲𝗮𝗿𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗦𝗤𝗟 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 In the real-world we work with a lot of data at once. This is not a nice-to-have; it’s a must-have skill. Q͟u͟e͟r͟y͟ ͟o͟p͟t͟i͟m͟i͟z͟a͟t͟i͟o͟n͟ ͟t͟i͟p͟s͟ ↳ Avoid unnecessary data processing ↳ Reduce dataset size early ↳ Use indexes wisely ↳ Use EXPLAIN Get practice optimizing your queries: www.interviewmaster.ai 𝟱. 𝗔𝗽𝗽𝗹𝘆, 𝗯𝘂𝗶𝗹𝗱, 𝗮𝗻𝗱 𝗶𝘁𝗲𝗿𝗮𝘁𝗲 Build your own projects. But what projects should you build? Here are some ideas: ↳ Analyzing student’s mental health: https://lnkd.in/gZCUPpr5 ↳ What and where are the world’s oldest businesses: https://lnkd.in/gSWSdVt3 ↳ NYC public school test result scores: https://lnkd.in/g-SCsY5M 𝟲. 𝗣𝗿𝗲𝗽 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗿𝗼𝗹𝗲𝘀 Learn how SQL is used in the real-world: https://lnkd.in/gZt6bp-F And, of course, practice for SQL interviews - LeetCode: https://lnkd.in/gpcyVPh9 - Interview Master: https://lnkd.in/gvs2u8Bm - StrataScratch: https://lnkd.in/g9D9jZ9A ——— Starting from scratch? Learn all your SQL fundamentals in one place: https://lnkd.in/gNXW297S