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Read More →Download the AI engineer learning path PDF from uncodemy: a seven-stage roadmap from Python to deployed LLM apps with outcomes and projects.
This page offers uncodemy's AI engineer learning path as a PDF download. It sets out the order in which to learn the skills, with an outcome and a project for each stage and realistic time ranges. It is a roadmap, not a promise of a job or salary.
Start with the main roadmap PDF. The extra cards below are for any companion files uncodemy publishes on this topic — download only what you need.
The full seven-stage roadmap from Python and software basics through data, maths, classical ML, deep learning and LLM applications to deployment and MLOps — with an outcome and a project for each stage and realistic time ranges for part-time study.
An optional second file on the same topic. Download this only if uncodemy publishes an additional companion PDF to the main roadmap; otherwise skip it and read the seven stages on this page instead.
An optional third file on the same topic. Same rule: download it only if uncodemy publishes an extra PDF that genuinely fits this learning path; otherwise use the stages and project ideas on this page.
Each download is a separate file. Show only as many cards as there are real PDFs — each with its own title, description, page count, size and file.
Many beginners jump straight to the newest model or framework and then discover that they cannot debug their data, read an error or explain a metric. A learning path avoids that by building skills in an order where each stage supports the next. The stages below are sequential for a complete beginner. If you already work as a developer or analyst, skip ahead to the first stage where you cannot yet complete the outcome without help. Time ranges are rough guides for a person studying part-time; your pace will differ.
Each stage pairs a focus area, a typical time range for part-time study, and the outcome you should be able to show when you finish it.
| Stage | Focus | Typical time | Outcome you should be able to show |
|---|---|---|---|
| 1 | Python and software basics | 4 to 8 weeks | A command-line program with functions, files and tests, stored in Git |
| 2 | Data handling and SQL | 4 to 6 weeks | A cleaned dataset and a short analysis written up |
| 3 | Maths and statistics for ML | 4 to 8 weeks, alongside stage 4 | Explain mean, variance, distributions, gradients and a confidence interval |
| 4 | Classical machine learning | 6 to 10 weeks | A tabular prediction project with honest evaluation |
| 5 | Deep learning | 6 to 10 weeks | A small neural network trained and evaluated with a framework |
| 6 | LLM applications | 6 to 10 weeks | A retrieval-based assistant with a test set and citations |
| 7 | Deployment and MLOps | 4 to 8 weeks | A containerised service with logging, monitoring notes and a CI run |
Learn Python syntax, data structures, functions, modules, exceptions, file handling and virtual environments. Add Git, the command line and basic testing with pytest. The goal is not clever code but reliable code: readable names, small functions and tests that fail when something breaks. Finish with a small project such as an expense tracker or a text-file analyser, published with a clear README.
Learn SQL querying with joins, aggregation and window functions, then pandas and NumPy for loading, cleaning, reshaping and summarising data. Practise on messy public datasets and document the decisions you made about missing values and outliers. Many AI problems are really data problems, so time here pays back later.
Study the maths in context rather than in isolation: use probability when you meet naive Bayes, linear algebra when you meet embeddings and gradients when you meet training. In classical machine learning, learn the workflow of splitting data, training a baseline, tuning, cross-validating and analysing errors. Work with linear and logistic regression, trees, random forests and gradient boosting. Always start with a simple baseline and compare any complex model with it. Write down the metric you chose and why.
| Project | Skills practised |
|---|---|
| Customer churn prediction from a public dataset | Classification, imbalance, feature engineering, metrics |
| House price estimation | Regression, feature scaling, error analysis |
| Sentiment classification of reviews | Text features, evaluation, baseline comparison |
Learn how neural networks are built from layers, how loss and optimisation work and how to train with PyTorch or TensorFlow. Train a small image or text model, track experiments and learn to recognise overfitting. Read an introduction to transformers so that you understand tokens, embeddings and attention at a conceptual level. You do not need to train a large model from scratch to build useful applications, but you need to understand what the models do.
Learn how to call a hosted or open model through an API, design prompts, request structured output, add retrieval from your own documents, let a model call tools and evaluate results. Build a small assistant on a document set, create a test set of questions with expected answers and measure quality, cost and response time. Treat model output as untrusted: validate, cite sources and design for the cases in which the model is wrong.
Wrap your model or application in an API, containerise it, add configuration through environment variables and deploy it to a cloud platform or a small server. Add logging, basic monitoring for latency, errors and cost, and a pipeline that runs tests on each change. Document how to reproduce training and how to roll back.
Keep yourself honest with a short check-in once a month. If the answer to a question is no, the next column tells you what to change.
| Question | If the answer is no |
|---|---|
| Did I finish one project piece this month? | Shrink the next project so it can be finished |
| Can I explain what I built without notes? | Review the concepts and write a short explanation |
| Did I test my work on data I did not train on? | Add a proper train, validation and test split |
| Is my work public with a README? | Publish it, even if imperfect |
Ready to follow the roadmap with a mentor? Explore uncodemy's Generative AI / Agentic AI course and learn from industry trainers.
