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Applied AI engineer
The four skills AI job posts name most: machine learning with scikit-learn, computer vision with OpenCV, deep learning with TensorFlow and Keras, and RAG chatbots with LangChain — each with a plant project.
Step 1AI and machine learningBeginnerMachine Learning with Python
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.
- Fit a regression whose coefficients you can explain in kWh per unit
- Split train and test without leakage — by time for logs, by motor for fleets
- Read a confusion matrix and choose recall or precision with the person who owns the cost
Step 2AI and machine learningBeginnerComputer Vision with OpenCV
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.
- Treat an image as an array and measure a part in millimetres from pixels
- Detect parts by colour in HSV and by brightness with Otsu and adaptive thresholds
- Clean binary images with morphology and separate touching parts
Step 3AI and machine learningIntermediateDeep Learning with TensorFlow and Keras
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's free GPU.
- Say when a neural network beats a random forest, and when it does not
- Build and train a Keras model with the right loss, early stopping and dropout, and read its curves
- Feed images from folders and signal windows from streams through a fast tf.data pipeline
Step 4AI and machine learningIntermediateRAG and Chatbots with LangChain
The skill Indian AI job posts name most often, built properly: 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 — on free models throughout.
- Load, clean and chunk vendor manuals so parameter tables survive
- Embed locally, search with hybrid BM25 + vector retrieval and re-rank with a cross-encoder
- Build a RAG chain that cites (doc p.N), refuses with NOT FOUND and handles follow-ups
How the path works
Join the path and every course is added to your account at once, each with its own progress, notes, quizzes and final. Take them in the order shown: each assumes the one before. Module quizzes are five plain questions and the final is ten, pass mark 40%, with unlimited retries, so the assessment checks that you followed the lessons rather than testing exam nerves.
Questions
- Is the Applied AI engineer free?
- Yes. Every course in the path is free in full: lessons, notes, practice tasks and assessments. Joining the path adds all of them to your account in one step. The only paid item is the optional EDWartens Certificate of Completion for each course.
- In what order should I take the courses?
- In the order listed: each course assumes the one before it. Every course keeps its own progress, quizzes and certificate, so you can also take one on its own.
- Do I get one certificate for the whole path?
- Certificates are per course, issued when you pass that course's final assessment and bought only if you want them. Finishing every course in a path gives you one certificate per course.