Codebasics

Most businesses have the data. What they don't have is someone who knows which questions to ask first. Kori Ganesh took on a real-world retail dataset: 500K+ rows of transactional data; and turned it into clear, actionable insights: a 90-day churn threshold to flag at-risk customers, a product health check that revealed a balanced sales mix, and a finding that 84.6% of revenue was concentrated in a single market. The tools were Python, Pandas, and Seaborn. But the real skill was knowing what the business needed to hear. That's the difference between writing code and doing analysis.

Most professionals are not ignoring AI. They are just consuming it without ever applying it. New tools drop every week. Newsletters stack up. Everyone has an opinion. But very few people actually know what works in real work, and that gap is quietly becoming a career gap. Knowing about AI and knowing how to use it are two very different things. The second one is what actually changes your output, your value, and your options. Codebasics built AI Pro specifically for non-tech professionals who want to stay current, practice on real problems, get guidance, and stay in a community that keeps them moving. Not a one-time course. Not another certification. A subscription built for a world that does not stop changing. If you have been meaning to get serious about this, this is the moment.

Some people wrap up in two hours what takes others all day. The work is the same. The approach is not. The people who finish first? They are not working harder. They figured out which parts of their job AI can handle, and built that into their week. That gap between you and them gets wider every month you wait. Codebasics AI PRO is a monthly subscription that keeps you ahead of the curve. Every month, you get the most relevant AI updates, filtered for what actually matters at work. No noise. Just what moves the needle. Join the waitlist.

5 AI projects worth building in 2026: 1. Production RAG System Not just retrieval. Add role-based access, evaluations (Ragas), guardrails, and actual cloud deployment. Use Langchain. Deploy on AWS, GCP, or Azure. 2. AI Voice Assistant Build with 11labs or VAPI. Voice is everywhere now. 3. Multi-Agent Coding Assistant One agent plans. Another codes. Another tests. Shows you understand orchestration, not just prompting. 4. Multi-Modal AI Assistant Text plus images. Most real problems aren't single-modality. 5. Hybrid Text Classification The industry standard: statistical ML for some cases, regex for others, LLM for the rest. Cost-effective and practical. These aren't beginner tutorials. They're the projects that show you think about real constraints: cost, deployment, monitoring, production readiness. We've built full guides with datasets and problem statements for each one. (check comments) The engineers who build portfolios like this don't send 100 applications. They get called in.

AI is no longer a nice-to-have skill limited to tech teams. It is becoming the difference between professionals who simply execute and those who grow in influence. The ones who understand it are not working harder. They are working faster, making better decisions, and getting trusted with bigger responsibilities. Without the right direction, AI feels scattered and difficult to apply to real work. What you need is clear thinking, practical use cases, and the confidence to use it where it actually matters. If you think it’s your time to move from “𝘐’𝘷𝘦 𝘩𝘦𝘢𝘳𝘥 𝘰𝘧 𝘈𝘐” to “𝘐 𝘤𝘢𝘯 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘶𝘴𝘦 𝘪𝘵”, check out the pinned caption to join the waitlist.

If you work with JavaScript or manage any web project, check your Axios version today. Two malicious versions of Axios were published this week: 1.14.1 and 0.30.4. Axios is the most downloaded npm package in the world, with over 100 million installs a week. The malicious code ran silently, collected sensitive data from your environment, sent it to an external server, and then deleted itself. No traces. Your system looks clean even if it was hit. The Axios team and npm pulled both versions within 3 hours. Fast response. But that window is enough to cause real damage. If you or anyone on your team installed either version, treat your credentials as compromised. Rotate API keys, passwords, and secret keys immediately. Then move to Axios 1.14.0. Pass this along to your team. The faster this spreads, the less damage it does.

Pricing is one of the hardest decisions a business makes. Too high, you lose customers. Too low, you leave money on the table. Most companies still rely on intuition for this. Karan Tatyaso Kamble built something better- an ML-powered pricing intelligence system for a beverage company. It takes in customer demographics, consumption patterns, and brand preferences, then predicts optimal price points backed by data. What makes this project stand out isn't just the model. It's the full pipeline thinking: data processing, feature engineering, an XGBoost model, a REST API via FastAPI, and a Streamlit interface so stakeholders can actually validate the predictions themselves. That's the difference between writing code and building a production-ready solution. If working on real-world projects like this interests you, explore our Gen AI & Data Science Bootcamp.

Your portfolio is more than a resume. It’s proof of what you can build.    To encourage this, we ran the Portfolio Website Challenge for our Bootcamp learners.   After reviewing many impressive submissions, we’re excited to announce the winners & top submission of the challenge! 🎉 Archit Kannan | Arjun K. | Anshuman Bordoloi | Soumyadeep Dhar | Rimon Ghosh | Rita Mahato | Lesie Fernandes | Sanjay Polamarasetti | Aishwarya Prabhakaran | Tejasree Pasalur This recognition is a small token of appreciation for the effort you put into building and presenting your work.   A big shoutout to all participants for taking the step to submit your portfolio! Building and showcasing your work is already a big step forward to being Job-ready. 👏 If you participated in the challenge, comment about your overall experience. We would love to hear it 👇 #codebasics #data #codebasicsportfoliowebsitechallenge

A small mistake I made while building with AI and a lesson worth sharing I once tried to build a complex application by giving a single large prompt Build the whole system The AI took time, generated a lot of output But the result was not usable That is when I realized 👉 AI works best with iterative development, not one-shot execution What works better • Build one feature at a time • Verify output at each step • Refine before moving forward This approach helps Reduce unintended bugs Maintain better control over the system Optimize token usage and cost Check out the Claude Code crash course Link in comments 👇 #AI #SoftwareEngineering #BuildInPublic #ProductDevelopment #Claude

Not a toy project. Not a tutorial clone. A production-style system built from scratch. This is what happens when you learn by building, not just watching. If this inspires you to start building, the Gen AI & Data Science Bootcamp is where projects like this come to life.