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Learning path · Aimed at an applied AI engineer

Applied AI engineer

For learners with some Python who want to build machine learning, vision, deep learning and chatbot projects.

You start with the first course. The next one is added when you are halfway through the one you are on.

See the courses in order
4 courses · 50 modules · 59h 8m of video · free, no card

What you can do at the end

Finish the path and you can do this

  • Train and test a machine learning model with scikit-learn.
  • Process images with OpenCV and train a network with TensorFlow and Keras.
  • Build a chatbot that answers from your own documents with RAG and LangChain.

A path teaches skills. It does not promise a job, an interview or a salary.

Why these courses together

Each course builds on the last

  • 1Machine learning first: deep learning and vision build on the same train, test and measure habits.
  • 2Each course ends with a plant project, so you finish with several things to show rather than one.
  • 3The RAG course is last because it joins models, data and text into something people can use.

A planned order also saves the time you would spend choosing what to learn next, and you finish with one story to tell: what you learnt, in order.

The roadmap

4 courses, in this order

  1. Step 1: Machine Learning with Python

    Adds: scikit-learn end to end on plant data: regression on energy against production, classification of motor-current signatures, the honest evaluation habits that make a model trustworthy, and a motor-fault classifier trained, tuned and reported the way an interviewer wants to see it.

    • Beginner
    • 13 modules
    • 12h 22m of video

  2. Step 2: Computer Vision with OpenCV

    Adds: Classical image processing on the pictures a plant produces: parts on a belt, caps on bottles, a scratch on a surface. Images as arrays, thresholds, morphology, edges, contours and measurement, video and background subtraction, tracking and counting, Hough and template matching, and the camera, lens and lighting decisions that come before any code.

    • Beginner
    • 12 modules
    • 15h 5m of video

  3. Step 3: Deep Learning with TensorFlow and Keras

    Adds: Neural networks for the data a forest cannot read: images of surfaces and windows of vibration. Neurons, loss and gradient descent in plain words, Keras models, training well, data pipelines, CNNs, augmentation and transfer learning, sequence models, evaluation and explanation, and deployment to TensorFlow Lite — all on Colab, with its free GPU when one is available.

    • Intermediate
    • 13 modules
    • 21h 42m of video

  4. Step 4: RAG and Chatbots with LangChain

    Adds: A chatbot that answers from PLC and drive manuals, cites the page, refuses what it cannot find, and is measured on a test set. Loading and chunking, embeddings and hybrid search, the RAG chain, query translation, routing, re-ranking, evaluation, LangGraph agents and serving, using free tools and the free tier of a hosted model (daily limits apply).

    • Intermediate
    • 12 modules
    • 10h 0m of video

4

courses

50

modules

59h 8m

of video in total

Example: at 5 hours a week, the video alone takes about 12 weeks. Practice tasks and the finals come on top, and you set the pace.

Practise what you learn in the free 3D lab

Technical work is judged on what you can do: wiring, commissioning, finding a fault. Doing a task yourself teaches more than watching it a second time, and a lab lets you try it safely before you meet real equipment. It does not replace working on real hardware.

See the 3D lab

What you get

What joining gives you

  • Every course is free, with lifetime access and no card.
  • Video lessons by credited independent creators, with our written notes and worked examples.
  • A practice task per module, and one final assessment per course.
  • Ask-an-engineer support.
  • Optional verifiable certificate for each course you pass, US$23.99 for a beginner course. There is no single certificate for the whole path.
  • One course at a time: you start with the first, and the next is added when you are halfway through it.
  • Each course keeps its own progress. You can open any course on its own at any time.

Put it into practice

Then try it in an internship

A laptop screen showing a model result plotted as a graph.

AI and Machine Learning Internship

Eight short weeks of machine learning from data to a deployed model: notes, diagrams, a few videos and worked exercises in free notebooks.

The AI and Machine Learning internship adds weekly projects to build on what you learnt here.

The Free internship is 4 weeks with a free certificate. It is training, not employment, and the Engineer-verified internship is optional.

See the AI and Machine Learning internship

Questions

Questions about this path

Is the Applied AI engineer free?

Yes. Every course in the path is free in full: lessons, notes, practice tasks and the final assessment. The only paid item is the optional EDWartens Certificate of Completion for each course.

What happens when I join the path?

You are enrolled on the first course straight away. The next course is added to your account when you are about halfway through the one you are on, so you never face a pile of unstarted courses. Each course keeps its own progress, notes and final.

In what order should I take the courses?

In the order listed: each course is placed after the ones it builds on. Every course also stands alone, so you can open any of them from the list.

Do I get one certificate for the whole path?

No. Certificates are per course, issued when you pass that course's final assessment, and you only buy one if you want it. Finishing every course gives you one optional, verifiable certificate for each.

How long will it take?

The video alone is about 59h 8m. Notes, practice tasks and the finals take extra time, and you go at your own pace.

Do I have to do the internship?

No. It is a separate, optional step. The AI and Machine Learning Internship is training, not employment: the Free internship is 4 weeks with a free certificate, and the Engineer-verified internship is optional.

Ready to start Applied AI engineer?

You start with the first course. The next one is added when you are halfway through the one you are on.

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