Learn AI and ML in this order: Python, SQL and pandas, statistics, classical machine learning, deep learning, LLMs with retrieval and agents, then deployment. Build a project at stages 4, 6 and 7 and you will have a portfolio most freshers lack.
- 1Pythonscripting, data structures
- 2Datapandas, SQL, cleaning
- 3Statisticsdistributions, tests
- 4Classical MLmodels, evaluation
- 5Deep learningPyTorch, transformers
- 6LLMs and RAGretrieval, evals
- 7DeployAPIs, monitoring
Learning path: free resources in order
| Stage | Free resource | Industry tool you will meet |
|---|---|---|
| 1. Python | Python tutorial (python.org) | Python, Jupyter, VS Code, Git |
| 2. SQL and data | SQLBolt · pandas: 10 minutes · NumPy absolute beginners | SQL databases, pandas, NumPy |
| 3. Maths and statistics | Mathematics for Machine Learning (free book) · Khan Academy statistics and probability | SciPy, statsmodels |
| 4. Classical ML | Google Machine Learning Crash Course · scikit-learn user guide · Kaggle Learn | scikit-learn, XGBoost, LightGBM |
| 5. Deep learning | Dive into Deep Learning · PyTorch tutorials | PyTorch, TensorFlow/Keras |
| 6. LLMs, RAG and agents | Hugging Face LLM course · Attention Is All You Need · LangChain docs | Hugging Face Transformers, LangChain, LangGraph, vector stores (FAISS, Chroma) |
| 7. Deploy | FastAPI tutorial · Docker get started | FastAPI, Docker, cloud platforms, experiment tracking |
| IIT lectures | NPTEL | Free recorded courses from IITs and IISc |
A realistic 12 to 24 week plan
| Weeks | Focus | Output you can show |
|---|---|---|
| Weeks 1 to 4 | Python, SQL, pandas | A cleaned dataset notebook with five questions answered |
| Weeks 5 to 8 | Statistics and classical ML | A churn or price model with a baseline and the right metric |
| Weeks 9 to 14 | Deep learning | A fine-tuned image or text classifier with error analysis |
| Weeks 15 to 20 | LLMs, RAG and agents | A document Q&A app with a 30-question evaluation table |
| Weeks 21 to 24 | Deployment | The same app behind an API in a container, with a README |
Roles in the industry
| Role | What the work looks like | Core tools |
|---|---|---|
| Data scientist | Frame a business question, build and evaluate models | Python, SQL, scikit-learn, notebooks |
| ML engineer | Turn models into reliable services | PyTorch, Docker, FastAPI, CI/CD |
| AI / LLM engineer | Build applications on language models with retrieval and tools | LLM APIs, RAG, LangChain/LangGraph |
| MLOps engineer | Monitor, version and retrain models in production | Experiment tracking, pipelines, cloud |
Mistakes to avoid
- Skipping the baseline: always report a simple model first so you can say what the complex one adds.
- Leaking test data into training (for example scaling before splitting).
- Reporting accuracy on imbalanced data; use precision, recall, F1 or PR curves.
- Building with an LLM before you can measure when it is wrong.
Build as you learn
AI & ML projects
Churn, fraud, recommenders and RAG, each with data and a metric.
Agentic AI projects
MCP servers, text-to-SQL and agentic RAG, each with an eval set.
AI project ideas
40 AI project ideas for 2026, from a spam classifier to RAG apps, agents and fine-tuning, each with a dataset or API and a stand-out upgrade.
AI projects with code
15 AI projects with source code on GitHub for 2026: LLM, RAG, agents, vision and ML repos, each checked for licence and recent commits.
Free tools for this field
Free AI tools for builders
Run, build and test AI projects without paying.
Free agentic AI tools
Frameworks and tools for building and testing AI agents.
Free LLM APIs and local models
Gemini, OpenRouter, Groq, Cloudflare and Ollama: real limits, and what ended.
Frequently asked questions
How do I start learning AI and machine learning from scratch?
Start with Python and SQL, then statistics, then classical machine learning with scikit-learn. Build a small model with a baseline before touching deep learning or LLMs.
How long does it take to become job-ready in ML?
With 8 to 10 hours a week, about six months to reach a portfolio with a deployed model and a measured LLM project. Timelines vary with your starting point.
Do I need a degree for AI and ML jobs?
Most roles ask for demonstrable skills and projects. A degree helps for research roles; for engineering roles, a deployed, measured portfolio matters more.
Which is better to learn first, deep learning or classical ML?
Classical ML. It teaches evaluation, leakage and baselines, which deep learning and LLM work depend on.
Learn this with mentors
AI and machine learning programmes with admissions open: live classes, mentors and project work.
