Complete Python for Everybody (Coursera, free audit) — cover functions, lists, dictionaries, file handling, and libraries. Also, install Jupyter Notebook and practise 30 minutes of coding daily. Furthermore, complete Khan Academy's Statistics and Probability course — mean, variance, probability distributions, hypothesis testing. Also, study linear algebra basics using 3Blue1Brown's Essence of Linear Algebra (YouTube — free and excellent). Build this month: a Python script that fetches data from a public API and performs basic statistical analysis. Publish on GitHub day one — your GitHub profile is your portfolio.

Start Andrew Ng's Machine Learning Specialization on Coursera (financial aid = free). Also, cover supervised learning, regression, classification, neural networks, and unsupervised learning over these two months. Furthermore, practise every concept on real datasets from Kaggle — use Indian datasets (stock prices, crop yield, crime data, election results) because they make your projects stand out as India-relevant. Also, start competing in beginner Kaggle competitions — even a rank in the bottom 40% shows you can apply skills to real problems. Build this month: a house price prediction model trained on Indian property data. Write a LinkedIn post explaining what you built and what you learned — this post alone can generate recruiter messages.

Complete DeepLearning.AI's Deep Learning Specialization (first two courses cover neural networks and improving deep learning — both free to audit). Also, separately, spend three weeks on LangChain fundamentals and the OpenAI API — build one LLM-powered application. Furthermore, this is the most important thing you can do: build a complete, deployed project using an LLM. Also, a simple chatbot that answers questions about a specific Indian topic (GST rules, UPSC syllabus, company policies) and is deployed on Streamlit or Hugging Face Spaces gives you a live, shareable URL that anyone can test. Furthermore, by month six, your GitHub should have three to five projects with clean READMEs, your LinkedIn should have the IBM Data Science or Google Data Analytics certificate, and you should be applying to 10 to 15 AI internships and entry roles per week on LinkedIn and Naukri.

Choose one specialisation: computer vision (learn OpenCV and YOLO), NLP and LLMs (fine-tuning with HuggingFace), or MLOps (learn Docker, FastAPI, and cloud deployment on AWS or Azure). Also, apply simultaneously — do not wait to "finish learning" before applying. Furthermore, technical AI interviews at Indian companies typically involve a coding round (LeetCode medium level), a machine learning conceptual round (explain model evaluation metrics, overfitting, bias-variance tradeoff), and a project walkthrough. Also, practise all three formats from month seven onwards. Furthermore, target first applications at mid-size Indian IT companies (Mphasis, Hexaware, L&T Technology Services), Indian AI startups (Sarvam AI, Observe.AI, Mad Street Den), and global companies with strong India engineering centres (Google, Microsoft, Amazon, Adobe).

Complete Google AI Essentials and Google Prompting Essentials — both available on Coursera with financial aid. Also, spend one hour daily across two weeks deeply exploring ChatGPT, Gemini, Claude, and Perplexity — not just chatting but testing their limits, failure modes, and best use cases in your domain. Furthermore, read "The AI Advantage" by Thomas H. Davenport (available in libraries and online) for understanding how AI creates business value. Also, subscribe to one quality AI newsletter — The Batch (DeepLearning.AI), TLDR AI, or Import AI — and read daily. Furthermore, build this month: a collection of 20 tested and refined prompt templates for your specific domain. A law student's collection of legal research prompts is more valuable than a generic prompt collection.

Complete one domain-specific AI course matching your background — AI for Finance (Coursera), AI in Healthcare (Stanford), AI for Marketing (HubSpot Academy), or AI Ethics (Montreal AI Ethics Institute). Also, learn to use no-code AI platforms: Make (formerly Integromat) for AI automation workflows, Notion AI for knowledge management, and Zapier AI Actions for connecting AI to business tools. Furthermore, spend two weeks learning the basics of data literacy with Google Sheets and basic Python through a single short course — you do not need to code, but reading a Python script and understanding what it does makes you significantly more valuable. Also, build this month: an AI-powered workflow solving a real problem in your domain. A B.Com student who builds an AI-assisted cash flow projection tool in Excel+ChatGPT has a portfolio piece that finance recruiters immediately understand and value.

By month five, you should have two or three completed AI projects in your domain, your Google AI Essentials certificate on LinkedIn, and a clear personal brand as someone who applies AI to a specific field. Also, write two or three LinkedIn posts or articles about what you have built — these posts generate recruiter outreach consistently. Furthermore, start applying specifically to roles titled: AI Trainer, Prompt Engineer, AI Content Specialist, AI Associate, and AI Product Coordinator — these are the non-technical AI roles with the clearest hiring path for your background. Also, apply to AI annotation companies like Scale AI, Appen, and iMerit which have India operations and consistently hire non-technical AI specialists. Furthermore, target companies adopting AI in your domain — banks, law firms, healthcare companies, ed-tech companies — as they need domain-expert AI users far more than they need generalist coders.