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Free AI courses for engineers with certificate

Twelve free AI courses written for people who work with machines: Python for engineering data, generative AI and LLM foundations, prompt engineering for engineering documents, AI agents and automation with n8n, machine learning, computer vision with OpenCV, deep learning with TensorFlow, RAG and chatbots with LangChain, and then the industrial track — AI for industrial automation, predictive maintenance, machine vision quality inspection and industrial data with Python. Every course has a real plant project with a dataset, marked as you submit, plus notes, quizzes and an optional verifiable certificate. Everything runs in a free Google Colab notebook and on free AI models; no paid API key is ever required.

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13 courses · 164 modules · 153h 58m of video
Python for AI and Engineering Data course cover
FreeAI and machine learningBeginner

Python for AI and Engineering Data

Python from zero, taught on the data an engineer actually has: tag lists, 4–20 mA readings, meter logs and motor currents. Variables to Pandas time series in twelve modules, all in a free Google Colab notebook, ending with a real year of plant energy data cleaned and reported.

  • Run Python in Google Colab and read an error message without panic
  • Convert 4–20 mA readings, scale signals and check NAMUR limits in code
  • Turn a vendor I/O list into a clean JSON tag map
13 modules 12h 19mView course
Generative AI and LLM Foundations course cover
FreeAI and machine learningBeginner

Generative AI and LLM Foundations

What a language model is actually doing when it answers, why it is confidently wrong sometimes, how to call one from Python on a free route, how to run one privately on your own laptop, and how to test one before you trust it with a datasheet. The concept course under the whole AI track.

  • Explain in plain words what an LLM does when it answers, and why it hallucinates
  • Choose a model for a job: context length, JSON output, local or hosted, licence
  • Write a prompt with role, task, context, format and a NOT FOUND rule
13 modules 10h 31mView course
Prompt Engineering for Engineers course cover
FreeAI and machine learningBeginner

Prompt Engineering for Engineers

Not fifty ChatGPT prompts. The skill of specifying a task to a language model so precisely that the output can be checked — applied to FAT reports, procedures, I/O lists, fault-code guides, code and spreadsheets — and the habit of testing a prompt with a golden set before trusting it.

  • Write a five-part prompt with a refusal rule that turns invention into a visible blank
  • Fix output format with few-shot examples and get JSON with a schema at temperature 0
  • Make calculations checkable with chain of thought, and run the arithmetic as code
12 modules 7h 34mView course
AI Agents and Workflow Automation with n8n course cover
FreeAI and machine learningBeginner

AI Agents and Workflow Automation with n8n

No-code automation with a language model in the loop, for the jobs an engineering office does by hand: triage maintenance tickets, send the 07:00 plant report, log a phone form into a register, watch a mailbox. n8n runs free on a laptop; the AI node points at a free model or a local Ollama. Every write goes through a human approval step.

  • Run n8n free on your own laptop and build a form-to-sheet-to-alert workflow
  • Read and shape data with expressions, Set, Split Out, Aggregate and Merge
  • Make a workflow safe to run unattended: retries, error workflow, idempotent writes
12 modules 19h 34mView course
Machine Learning with Python course cover
FreeAI and machine learningBeginner

Machine 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
13 modules 12h 6mView course
Computer Vision with OpenCV course cover
FreeAI and machine learningBeginner

Computer 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
12 modules 13h 46mView course
Deep Learning with TensorFlow and Keras course cover
FreeAI and machine learningIntermediate

Deep 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
13 modules 21h 26mView course
RAG and Chatbots with LangChain course cover
FreeAI and machine learningIntermediate

RAG 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
12 modules 9h 35mView course
AI for Industrial Automation course cover
FreeAI and machine learningIntermediate

AI for Industrial Automation

The hub of the industrial track: where AI actually sits beside PLC, SCADA, MES and the historian; which of the seven use cases pay and what data each needs; OPC UA, MQTT and the unified namespace; edge against cloud; predictive maintenance as a business case; vision, digital twins, copilots and agents; the vendor landscape; safety, security and governance — ending with a readiness audit of a real process.

  • Place any AI proposal on the control hierarchy and say what the PLC keeps
  • Match a plant problem to one of seven use cases and name the label it needs
  • Get data to a model with OPC UA, MQTT and a unified namespace without loading the PLC
13 modules 6h 27mView course
Predictive Maintenance with Machine Learning course cover
FreeAI and machine learningIntermediate

Predictive Maintenance with Machine Learning

The industrial AI application with a budget behind it, built end to end: failure modes and what each sensor sees, signals to condition indicators, anomaly detection on normal data, fault classification, deep learning on windows, remaining useful life on NASA's run-to-failure engines, and the alerting and deployment that decide whether anyone acts.

  • Choose the sensor by the warning time you need, and name the failure mode from its spectrum
  • Turn windows into condition indicators — kurtosis, band energies, envelope peaks — and rank them
  • Build a per-asset anomaly detector on healthy data with a threshold and persistence that stop alert floods
12 modules 10h 3mView course
Machine Vision and Quality Inspection course cover
FreeAI and machine learningIntermediate

Machine Vision and Quality Inspection

The whole inspection station, not just the model: cameras, lenses, lighting and triggers that make the image; classical tools for what can be measured; deep learning for what can only be shown; datasets and labelling; training, evaluating and exporting a YOLO defect detector; and deploying it beside a PLC that keeps the reject decision.

  • Turn feature size, field of view and belt speed into pixels, exposure and a lens focal length
  • Choose backlight, dark-field, dome or structured light by measured contrast, not opinion
  • Build the timing budget to the rejector and the PLC handshake with a heartbeat
12 modules 7h 56mView course
Industrial Data with Python course cover
FreeAI and machine learningIntermediate

Industrial Data with Python

The plumbing nobody teaches: values out of PLCs and meters over Modbus and OPC UA from Python, published over MQTT, stored as time series, cleaned and gridded, forecast and scored for anomalies, and wired to a dashboard — every stage built against a simulator on your own laptop, so no plant is needed and nothing you break matters.

  • Read holding registers over Modbus TCP and RTU from Python and decode floats, offsets and scaling correctly
  • Run an OPC UA server and client in Python, subscribe to changes and log value, quality and source timestamp
  • Design MQTT topics and payloads and bridge OPC UA to a broker with retain and last will
12 modules 10h 37mView course
MLOps Model Deployment and Monitoring course cover
FreeAI and machine learningIntermediate

MLOps Model Deployment and Monitoring

The half of machine learning that starts after the notebook: a reproducible environment and a baseline model; experiment tracking, model management and the registry with MLflow; turning a notebook into a pipeline that can be re-run and backfilled; batch, web-service and streaming deployment in Docker; monitoring data quality, drift and model quality with Evidently, PostgreSQL and Grafana; and the tests, linting, make targets and GitHub Actions pipeline that let a release happen without anybody copying a file to a server.

  • Log parameters, metrics and artefacts to MLflow and choose a model on the record rather than on memory
  • Register a model version, promote it, and roll back to the one it replaced
  • Turn a training notebook into a parameterised script a scheduler can re-run and backfill
15 modules 12h 5mView course

Why learn AI and machine learning in India

Indian employers posted roughly 290,000 AI-linked roles in 2025 and AI-engineer titles grew about 67% year on year, yet almost none of the free AI training on the internet is written for plant, panel and PLC people. Manufacturing is the slice of that demand nobody trains for, and it is the slice EDWartens learners already work in: predictive maintenance, vision inspection and plant data are where an automation engineer's AI skills are worth the most.

Questions

Which AI course should I start with?
Python for AI and Engineering Data if you have never coded, then Machine Learning with Python. If you already write Python, start with Generative AI and LLM Foundations or go straight to the industrial track with AI for Industrial Automation.
Do I need a GPU or a paid ChatGPT account?
No. Every project runs in a free Google Colab notebook, which gives you a GPU when a course needs one, and the language-model exercises use free models through OpenRouter or a local Ollama install. Nothing in any course asks for a paid key or a card.
What are the projects?
Real plant scenarios with a public dataset: clean a year of energy-meter logs, classify motor faults, count parts on a conveyor, predict remaining useful life on run-to-failure engine data, train a defect detector, log a simulated PLC over OPC UA. You submit a write-up and it is marked against a rubric within about a minute; resubmit as often as you like. One submitted project is part of every AI certificate.
Is the certificate an AI vendor credential?
The EDWartens Certificate of Completion is optional, paid, verifiable by code and issued after the final assessment. It is not a vendor credential.