👋 Introduction
My daughter will be starting her B.Tech in Computer Science at MIT, Manipal this year. As a huge AI proponent, I often share the latest AI trends and tools with my family. When my daughter decided to pursue CS, she asked me several questions about AI, which inspired this blog. I hope this guide helps any student planning to specialize in CS and AI.
📚 Core Fundamentals for CSE Students
Before diving into AI, it’s crucial to master the basics. These are some of the building blocks for everything you’ll do in computer science. Following links will give you an overview of the basics before you deep-dive.
- Data Structures & Algorithms: FreeCodeCamp
- Operating Systems: Coursera Course, High-Level Overview
- Programming Basics: Focus on logic and problem-solving, applicable to any language.
- Systems Design Basics: Gaurav Sen’s YouTube Channel
📝 General Advice for Students
In addition to doing your coursework, following tips can help you to be more practically prepared for the industry .
- Start with Fundamentals: Focus on math, programming, data structures, and algorithms.
- Build a Portfolio: Work on projects, participate in Kaggle competitions and hackathons, and maintain GitHub repositories.
- Network: Join AI clubs, attend meetups, and connect with peers and professionals on LinkedIn.
- Stay Updated: Follow AI news, research, and trends.
- Internships: Real-world experience is invaluable—seek internships early.
🛠️ Tools to Try Out
Following is just a sample collection at this point of time. The tools change so fast so it’s very important to keep yourself updated with the latest.
- Chatbots: ChatGPT, Gemini (Try ChatLLM, an aggregator of chatbots and other AI tools collection, its very handy)
- Vibe Coding: Cursor, Windsurf, Replit, Pythagora (see my earlier blog for more)
- Image Generation: DALL-E(OpenAI), Midjourney
- Video Generation: Google Veo
- ML Platforms: Google AI Studio(Good to experiment with Google AI models), Kaggle(Kaggle competitions are good, good for datasets and notebooks), Hugging Face(Marketplace for models, datasets and easy to share the ML work with others)
- Automation: Zapier (AI orchestration platform connecting different AI and non-AI tools and platforms)
Note: “Vibe coding” refers to using AI-powered coding environments that help you code faster and more intuitively.
🤖 Exploring AI Domains & Career Paths
Here’s a quick overview of different AI roles, what they do, prerequisites, and how to get started. AI industry is still at its nascent stage, these roles can change as the technology matures.
| Role | What They Do | Prerequisites | How to Get In |
|---|---|---|---|
| AI Researcher | Develop new AI models/algorithms, advance the field, publish research | Strong math (linear algebra, stats), deep ML/DL, Python, PyTorch/TensorFlow, research skills, academic writing | Advanced courses (Master’s/PhD), join research labs, open-source, publish papers, attend conferences |
| ML Engineer | Build, optimize, and deploy ML models in production; manage ML systems | Programming (Python, C++/Java), ML frameworks, software engineering, cloud (AWS/GCP/Azure), MLOps basics | End-to-end ML projects, internships, open-source, learn CI/CD, Docker/Kubernetes, model deployment |
| Data Engineer/Scientist | Build data pipelines, clean/process data, extract insights, visualize findings | Python, SQL, data wrangling, statistics, data viz, ML basics, big data tools (Spark, Hadoop) | Data science/engineering courses, Kaggle, portfolio projects, internships, learn data tools and visualization |
| AI Application Engineer | Integrate AI models into real-world apps/products; focus on APIs and UX | Programming (Python, JS, etc.), API development, front/back-end, basic ML, UX/UI | Build AI apps, hackathons, internships, learn REST APIs, cloud deployment |
| AI Security & Safety | Ensure AI systems are secure/safe; address ethical, legal, and risk concerns | Security fundamentals, cryptography, adversarial ML, AI ethics, risk, regulations, ML basics | Cybersecurity/AI ethics courses, CTFs, follow AI safety research, join labs/organizations |
| AI Product Manager | Define vision/strategy for AI products; bridge tech and business teams | AI/ML concepts, product management, communication, business acumen, user research | Start as engineer/analyst, PM courses, AI projects, internships, develop leadership/communication |
| AI Hardware Specialist | Design/develop hardware/software (GPUs, TPUs, SDKs) for AI training/inference | ECE/CS, digital design, computer architecture, parallel computing, C/C++, CUDA, ML basics | ECE/CS courses, hardware internships, FPGA/GPU projects, hardware-software co-design, follow NVIDIA/AMD/Intel |
🧑💻 AI Basics for Students
Following is just a sample to get started with AI basics.
- Teachable Machine by Google: Learn about ML models (eg: by training one to distinguish between a cat and a dog)
- Tensorflow playground
- Intro to TensorFlow Code playground (YouTube)
- Basics of Generative AI (Google Cloud Skills Boost)
- Introduction to Large Language Models (Google Cloud Skills Boost)
- AI for Everyone (Coursera): 5-hour course for all backgrounds.
- Machine Learning Introduction (Coursera): Covers supervised/unsupervised learning, popular ML libraries, and neural networks.
- Deepdive into LLMs – Andrej Karpathy
- How I use LLMs – Andrej Karpathy
🤔 How Should College Students Use AI (and How Not To)?
- Don’t: Use AI chatbots to solve class assignments directly—this can kill creativity and hinder learning.
- Do: Use AI as a learning tool to explore new ideas, get feedback on completed assignments, and clarify concepts after self-study.
- Tip: Treat AI as a personalized teacher—seek help only after you’ve tried solving problems yourself.
🔄 Staying Updated with AI
- Curate Resources: Make a repository of your favorite podcasts, blogs, and YouTube channels.
- Hands-On Practice: Try new AI tools and work on personal projects.
- Mix Coding Styles: Combine “vibe coding” (AI-assisted) with traditional coding to strengthen your skills.
💡 Is AI Going to Take My Job?
A typical software engineer spends only 30–40% of their time coding; the rest involves architecture, design, spec reviews, cross-functional discussions, integration testing, and release processes. While AI can assist with coding, these other activities are equally critical and difficult to automate.
Even within coding, engineers must structure code, manage module interactions, choose technologies, debug, test, scale, and deploy—tasks that require human judgment. AI coding tools can boost productivity by 30–40% today, and possibly up to 70% in the next 1–2 years. However, over-reliance on these tools can erode core skills, and poorly organized AI-generated code can become hard to maintain.
There’s no substitute for strong design and coding fundamentals. Use AI tools as an assistant, not a replacement.
Jevons Paradox: If coding becomes much easier and cheaper, we’ll see more coding projects and more coders, not fewer. The demand for skilled engineers will grow as we automate more of the world.
For the next 5–10 years, CS engineers will remain essential. If AI ever surpasses humans in all aspects (AGI), it won’t just be engineers—every profession will be affected.
🌱 Final Thoughts
CS or CS with AI specialization are fields of endless possibility. Stay curious, keep building, and remember: the journey is as important as the destination. Embrace change, focus on fundamentals, and use AI as a tool to amplify your learning and creativity.
Wishing all new B.Tech CS students an exciting and rewarding journey ahead!
Picture with my lovely daughter!
Sreenivas Makam's Blog 