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.

Is there an AI course for PLC programmers?

Yes. AI for PLC Programming teaches Structured Text with ChatGPT-style assistants and the Siemens Eigen Engineering Agent for TIA Portal, and how to review and test AI-written code before it reaches a machine.

Do you prepare for AI certification exams?

Yes: AWS Certified AI Practitioner (AIF-C01), Microsoft Azure AI Fundamentals (AI-901), Azure AI Apps and Agents Developer (AI-103) and Google Cloud Generative AI Leader. These are preparation courses; the exams are booked and paid with the vendor, and the EDWartens certificate is a certificate of completion.

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.

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Free AI courses for engineers with certificate

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, deep learning with PyTorch, AI agents and MCP, private AI with local LLMs, and then the industrial track — AI for PLC programming, AI for industrial automation, predictive maintenance, machine vision quality inspection, edge AI and TinyML on sensors, and industrial data with Python — plus exam preparation for the AWS, Microsoft and Google AI certifications. 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.

Browse all free courses

26 courses · 337 modules · 295h 24m of video · Updated 27 September 2026

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 22mView course

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

FreeAI and machine learningBeginner

Prompt Engineering for Engineers

A course for engineers on writing AI prompts with checkable output and testing them on a golden set. Free to learn; the certificate is optional and paid.

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 45mView course

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

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 22mView course

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 15h 5mView course

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 42mView course

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 11h 4mView course

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 38mView course

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 9h 46mView course

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

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 21mView course

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

FreeAI and machine learningIntermediate

ISO 42001 and AI Governance

The standard customers have started asking for by name: what an AI management system is, ISO/IEC 42001 clause by clause from context and leadership through the clause 6.1.2 risk assessment and the clause 6.1.4 impact assessment to documented information, the Annex A controls on policy, roles, resources, lifecycle, data, use and third parties, operation, internal audit, management review and certification, the NIST AI RMF and the OECD principles, training data and intellectual property, and the EU AI Act obligations an Indian supplier is asked to evidence.

Scope an AIMS and write an AI policy that top management can sign and staff can follow
Build an AI system inventory that turns up the tools nobody registered
Run the clause 6.1.2 risk assessment against criteria set before any scoring, in both directions of risk

13 modules 5h 38mView course

FreeAI and machine learningIntermediate

AI Security and the OWASP Top 10 for LLMs

The security course for people who have already built something with an LLM: the attack surface a model adds, all ten 2026 OWASP LLM risks one at a time, indirect injection and zero-click agent attacks, what leaks out through a chatbot, securing agents with identity and least privilege, the OWASP MCP and agentic guidance, red-teaming with MITRE ATLAS, and the NIST-based governance a plant or a GCC can actually enforce.

Draw an AI feature as its parts and mark where an outsider's text can enter
Name all ten 2026 OWASP LLM risks and sort a real incident into the right one
Trace an indirect injection from a poisoned document to an executed action

15 modules 9h 21mView course

FreeAI and machine learningBeginner

AI Coding for Engineers: Claude Code, Cursor and Copilot

Use AI coding agents the way an engineer should: set up Claude Code, Cursor and GitHub Copilot, write the spec before the prompt, review every diff, let tests and Git catch the mistakes, connect tools with MCP, keep secrets out, build a Python Modbus and OPC UA logger, and generate PLC Structured Text that is checked and simulated before it goes near a machine.

Explain what a coding agent does and which engineering tasks to give it
Install and configure Claude Code, Cursor and GitHub Copilot with safe permission modes
Write a one-page spec with acceptance tests before prompting

13 modules 10h 13mView course

FreeAI and machine learningBeginner

Microsoft Azure AI Fundamentals (AI-901) Exam Preparation

Preparation for Microsoft's AI-901 exam, built on the skills measured as of 15 April 2026: AI workloads and responsible AI, how generative models work and how to choose and deploy one, Microsoft Foundry projects and the model catalogue, prompts and a Python chat client, a single agent, text analysis, speech, vision and image generation, and information extraction with Content Understanding. Preparation for the exam, not the Microsoft certification itself.

Map your study to the AI-901 skills measured as of 15 April 2026 and their weights
Match scenarios to AI workloads and to Microsoft's six responsible AI principles
Explain tokens, embeddings, temperature and top-p, and choose a model and deployment type

12 modules 7h 57mView course

FreeAI and machine learningIntermediate

Private AI with Local LLMs: Ollama, Open WebUI and Offline RAG

Run AI on your own hardware so confidential drawings, specifications and plant data never leave the building: open-weight models and their licences, GPU and RAM sizing, quantisation, Ollama, Open WebUI for a team, the API and Python, offline RAG with citations, datasheet extraction, vision models, benchmarking, air-gapped deployment and an acceptable use policy.

Decide which documents may go to a hosted AI tool, which must stay on a local model and which go nowhere
Compare open-weight models and their licences: Apache 2.0, MIT and the Llama community licence
Size GPU memory and speed for a model from its parameters, quantisation, context length and users

13 modules 10h 40mView course

FreeAI and machine learningIntermediate

AI for PLC Programming: Structured Text, TIA Portal and Siemens Eigen

Use ChatGPT, Claude, local models and Siemens' TIA Portal agent to write, convert, document and test PLC code without trusting it blindly: Structured Text and SCL, specifications an AI can follow, importing generated code into TIA Portal, ladder and legacy conversion, CODESYS and TwinCAT interfaces, tag and alarm lists, simulation in PLCSIM and Factory I/O, TcUnit tests, a code review checklist and a team AI policy.

Decide which PLC tasks to give an AI model and which never to, and why safety logic stays out
Write an I/O list, sequence, interlock table and alarm list that an AI can code from without guessing
Generate SCL with ChatGPT or Claude, import it into TIA Portal as an external source and fix its errors

13 modules 11h 16mView course

FreeAI and machine learningBeginner

AWS Certified AI Practitioner (AIF-C01) Exam Preparation

Preparation for the AWS Certified AI Practitioner exam (AIF-C01), organised around the five domains of the exam guide revised on 30 April 2026: AI and ML fundamentals, metrics and the ML lifecycle, managed AI services, generative AI and token cost, Amazon Bedrock, RAG and Knowledge Bases, agents with MCP, Strands Agents, AgentCore and Kiro, prompt engineering, customising and evaluating models, responsible AI and Guardrails, and security and governance. Preparation for the exam, not the AWS certification itself.

Map the five AIF-C01 domains and the April 2026 revision to a study plan
Choose a learning type and technique, and calculate precision, recall and F1
Match business needs to AWS managed AI services by input and output

14 modules 9h 23mView course

FreeAI and machine learningIntermediate

AI Agents and MCP for Engineers

Build AI agents that read plant data safely: the agent loop, tool calling from Python, the Model Context Protocol, FastMCP servers for a SQL historian, OPC UA and Modbus that are read only by design, hosts from Claude to a local Ollama model, the OpenAI Agents SDK, LangGraph and Microsoft Agent Framework, a maintenance triage agent with human approval, guardrails, evaluation and deployment across network zones.

Explain the agent loop and choose between a workflow and an agent
Write tool definitions and a tool-calling loop in Python, including against a local model in Ollama
Describe MCP hosts, clients, servers, tools, resources and prompts as of the 2026-07-28 specification

13 modules 12h 36mView course

FreeAI and machine learningIntermediate

Azure AI Apps and Agents Developer (AI-103) Exam Preparation

Preparation for Microsoft's AI-103 exam, built on the skills measured as of 16 April 2026: planning, securing and monitoring Microsoft Foundry solutions, generative apps and RAG, agents with tools, MCP and multi-agent workflows, vision, text, speech and information extraction with Content Understanding and Azure AI Search. Preparation for the exam, not the Microsoft certification itself.

Map your study to the AI-103 skills measured as of 16 April 2026 and their weights
Choose a model, a deployment type and the Foundry services for a stated scenario
Secure a Foundry solution with managed identities, least-privilege roles and private endpoints

15 modules 14h 19mView course

FreeAI and machine learningBeginner

Google Cloud Generative AI Leader Exam Preparation

Preparation for Google Cloud's Generative AI Leader exam, organised around the four sections of the current exam guide: gen AI fundamentals, Google Cloud's gen AI offerings, techniques to improve model output, and business strategy. Covers Gemini, Gemma, Imagen and Veo, Gemini Enterprise and Gemini Enterprise Agent Platform (the successor to Vertex AI), Gemini in Workspace and Gemini Notebook, agents and tooling, prompting, grounding and RAG, the Customer Engagement Suite and AI APIs, SAIF and responsible AI, and choosing and measuring use cases. Preparation for the exam, not the Google Cloud certification itself.

Map the four Generative AI Leader sections and the 2026 product renames to a study plan
Explain foundation models, learning approaches, data quality and the ML lifecycle
Place Google's products in the five gen AI layers and choose between Gemini, Gemma, Imagen and Veo

12 modules 11h 35mView course

FreeAI and machine learningIntermediate

Edge AI and TinyML for Industrial Sensing

Put machine learning on the sensor: vibration, current and sound from motors, fans and pumps, sampled correctly, turned into spectral features, classified or scored for anomalies by a tiny model on an ESP32 or Arduino, quantised to fit, sent over MQTT or Modbus to a Node-RED dashboard, and deployed with a battery budget, updates and a drift check. Vision on a Raspberry Pi AI HAT and Jetson too. Hardware optional.

Decide which plant AI belongs on the sensor node and which in the cloud, with the data rates worked out
Choose between ESP32, Arduino Nano 33 BLE Sense, STM32, Raspberry Pi with an AI HAT and Jetson from memory, compute and power
Set a sample rate from shaft and bearing frequencies, and read vibration, motor current and sound safely

13 modules 10h 18mView course

FreeAI and machine learningIntermediate

Deep Learning with PyTorch for Engineers

Deep learning in PyTorch on engineering data: tensors and autograd, nn.Module and a training loop you write yourself, Datasets and DataLoaders over sensor logs, CNNs and transfer learning for defect images, 1-D CNNs and LSTMs for vibration and process tags, experiment tracking, Hugging Face models, and export to ONNX for a line PC.

Create, reshape and broadcast tensors, and move models and data between CPU, CUDA and Apple MPS
Work gradients by hand with the chain rule and check them with autograd
Write nn.Module networks and a full training loop with validation, early stopping and the best checkpoint

13 modules 16h 16mView course

FreeAI and machine learningBeginner

Oracle Cloud AI Foundations Exam Prep (1Z0-1122)

Preparation for Oracle's Oracle Cloud Infrastructure AI Foundations Associate exam (1Z0-1122-26): AI, machine learning and deep learning foundations, generative AI and LLMs, prompts, RAG and fine-tuning, the OCI AI services, OCI Data Science and GPU infrastructure, OCI Generative AI and its agents, AI Vector Search and Select AI, and responsible AI. Preparation for the exam, not the Oracle certification itself.

Map your study to Oracle's 1Z0-1122-26 topic areas and book the exam on Oracle MyLearn
Tell supervised, unsupervised and reinforcement learning apart and compute precision, recall and RMSE
Explain neurons, activation functions, gradient descent, CNNs, RNNs and LSTMs with worked numbers

14 modules 8h 43mView course

Compare the AI and machine learning courses

Course	Level	Modules	Video	Brand
Python for AI and Engineering Data	Beginner	13	12h 22m	Vendor-neutral
Generative AI and LLM Foundations	Beginner	13	10h 31m	Vendor-neutral
Prompt Engineering for Engineers	Beginner	12	7h 45m	Vendor-neutral
AI Agents and Workflow Automation with n8n	Beginner	12	19h 34m	Vendor-neutral
Machine Learning with Python	Beginner	13	12h 22m	Vendor-neutral
Computer Vision with OpenCV	Beginner	12	15h 5m	Vendor-neutral
Deep Learning with TensorFlow and Keras	Intermediate	13	21h 42m	Vendor-neutral
RAG and Chatbots with LangChain	Intermediate	12	11h 4m	Vendor-neutral
AI for Industrial Automation	Intermediate	13	6h 38m	Vendor-neutral
Predictive Maintenance with Machine Learning	Intermediate	12	9h 46m	Vendor-neutral
Machine Vision and Quality Inspection	Intermediate	12	7h 56m	Vendor-neutral
Industrial Data with Python	Intermediate	12	10h 21m	Vendor-neutral
MLOps Model Deployment and Monitoring	Intermediate	15	12h 5m	Vendor-neutral
ISO 42001 and AI Governance	Intermediate	13	5h 38m	Vendor-neutral
AI Security and the OWASP Top 10 for LLMs	Intermediate	15	9h 21m	Vendor-neutral
AI Coding for Engineers: Claude Code, Cursor and Copilot	Beginner	13	10h 13m	Vendor-neutral
Microsoft Azure AI Fundamentals (AI-901) Exam Preparation	Beginner	12	7h 57m	Vendor-neutral
Private AI with Local LLMs: Ollama, Open WebUI and Offline RAG	Intermediate	13	10h 40m	Vendor-neutral
AI for PLC Programming: Structured Text, TIA Portal and Siemens Eigen	Intermediate	13	11h 16m	Vendor-neutral
AWS Certified AI Practitioner (AIF-C01) Exam Preparation	Beginner	14	9h 23m	Vendor-neutral
AI Agents and MCP for Engineers	Intermediate	13	12h 36m	Vendor-neutral
Azure AI Apps and Agents Developer (AI-103) Exam Preparation	Intermediate	15	14h 19m	Vendor-neutral
Google Cloud Generative AI Leader Exam Preparation	Beginner	12	11h 35m	Vendor-neutral
Edge AI and TinyML for Industrial Sensing	Intermediate	13	10h 18m	Vendor-neutral
Deep Learning with PyTorch for Engineers	Intermediate	13	16h 16m	Vendor-neutral
Oracle Cloud AI Foundations Exam Prep (1Z0-1122)	Beginner	14	8h 43m	Vendor-neutral

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.
Is there an AI course for PLC programmers?: Yes. AI for PLC Programming teaches Structured Text with ChatGPT-style assistants and the Siemens Eigen Engineering Agent for TIA Portal, and how to review and test AI-written code before it reaches a machine.
Do you prepare for AI certification exams?: Yes: AWS Certified AI Practitioner (AIF-C01), Microsoft Azure AI Fundamentals (AI-901), Azure AI Apps and Agents Developer (AI-103) and Google Cloud Generative AI Leader. These are preparation courses; the exams are booked and paid with the vendor, and the EDWartens certificate is a certificate of completion.
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.

Classroom course, Bangalore

PLC training in Bangalore

The Automation Engineer Program (AEP) is our classroom course in PLC programming and industrial automation, taught in person at our Electronic City centre.