AI and machine learning · Free
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.
Inside the course
From the lessons
What a neural network is, and why now
codebasics
How it learns: gradient descent and backpropagation
codebasics
Training well: dropout, early stopping, imbalance
codebasics
Convolutional networks
codebasics
Sequences: 1-D CNNs, RNNs, LSTMs and autoencoders
codebasics
Saving, TensorFlow Lite and the edge
codebasics
Lesson frames belong to the creators named in the Credits below and are shown from YouTube.
What you will learn
Explain what a network learns and when to use one; pair activations with losses; build, train and regularise Keras models; feed images and signal windows through tf.data; train CNNs and use transfer learning on small datasets; use 1-D CNNs, LSTMs and autoencoders on sensor windows; evaluate with per-class recall and Grad-CAM; export to TensorFlow Lite and measure latency.
- 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
- Train a CNN on surface defects and lift it with augmentation and transfer learning
- Classify vibration windows with a 1-D CNN and score anomalies with an autoencoder
- Report per-class recall, explain a prediction with Grad-CAM and export to TensorFlow Lite
For you
Taking Deep Learning with TensorFlow and Keras from the United States
- Free in the United States, as everywhere, and self-paced: lessons, notes and the final assessment are open at any hour, so your time zone and shift pattern do not matter.
- The optional certificate for learners in the United States is a one-off US$28.99. What you get for it
- Plants across the Americas most often run Allen-Bradley, Siemens and Inductive Automation; each has its own free course to take next.
- See automation and engineering jobs in the United States, and what the industry looks like in Houston, Detroit and Chicago.
- EDWartens also has a regional site for the United States, for classroom training and local support: edwartens.com/us.
The course project · about 6 hours
A surface-defect classifier: baseline CNN, augmentation, transfer learning — one table
1,200 greyscale 128×128 surface images in four classes (ok, scratch, dent, blob). Split by file once, train three models on Colab's free GPU, report them on one sealed test set with per-class recall, explain three predictions with Grad-CAM, and export a TFLite model with its size.
The course project · about 4 hours
Vibration windows: a 1-D CNN against an LSTM for bearing and imbalance faults
600 windows of 256 samples at 2 kHz labelled healthy, bearing or imbalance. Standardise per window, split by window index, train a 1-D CNN and an LSTM, compare test confusion matrices, and add an autoencoder anomaly score trained on healthy windows only.
Course content
13 modules · 42 lessons · 21h 42m
In order, at whatever pace suits you. Each module ends with a practice task that builds on the last.
- 01What a neural network is, and why now5 lessons2h 10m
- 02The neuron, activation and loss3 lessons58m
- 03How it learns: gradient descent and backpropagation5 lessons1h 58m
- 04Keras Sequential: your first model3 lessons8h 9m
- 05Training well: dropout, early stopping, imbalance3 lessons1h 9m
- 06Data pipelines: tf.data for images and windows2 lessons60m
- 07Convolutional networks4 lessons1h 8m
- 08Augmentation and transfer learning3 lessons1h 10m
Requirements
- Who it is for
- Intermediate. Needs the Machine Learning with Python course, or equivalent: train/test discipline, metrics, Pandas.
- Software
- Google Colab with a free GPU runtime. TensorFlow and Keras are pre-installed. What to download, and how
- Hardware
- None. A Raspberry Pi is optional for the deployment module.
Software you need
What to download, where from, what it costs and how to install it. Every link goes to the maker's own site, never a mirror.
Colab's free GPU runtime runs every exercise. TensorFlow and Keras are already installed there.
Required
- 01Free, GPU time not guaranteed
Google Colab
Google, in the browser
- Runs on
- Any modern web browser
- Account
- A free Google account
Colab is free to use. In the free version GPUs and TPUs are heavily restricted and not guaranteed, sessions can run for at most 12 hours, and idle sessions are stopped. Paid plans give more reliable access.
Steps
- 1.Open colab.research.google.com and sign in with your Google account.
- 2.Click New notebook (or open the notebook your course links to).
- 3.For a GPU, choose Runtime, then Change runtime type, then pick a GPU if one is offered.
- 4.Type code in a cell and press Shift and Enter to run it.
- Save a copy to your Google Drive so your changes are kept.
- Files on the runtime are deleted when the session ends. Save outputs to Drive.
Open Google Colabcolab.research.google.com
Optional
Useful, not needed to finish the course.
- 02Free
TensorFlow
Google (TensorFlow project), a Python or npm package
- Runs on
- Python 3.10 to 3.13. Ubuntu 16.04 or later; macOS 12 or later (CPU only); Windows 7 or later (CPU only); Windows 10 build 19044 or later through WSL2 for GPU.
- Account
- None needed
Free, open source under the Apache 2.0 licence.
Steps
- 1.Install a supported Python (3.10 to 3.13) and create a virtual environment.
- 2.Upgrade pip: pip install --upgrade pip
- 3.For CPU: pip install tensorflow
- 4.For an NVIDIA GPU on Linux or WSL2: pip install tensorflow[and-cuda]
- 5.Test it: python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
- TensorFlow 2.10 was the last release with GPU support on native Windows. For a GPU on Windows, install inside WSL2.
- If you have no GPU, use Google Colab to train larger models.
Official download pagetensorflow.org
Checked against each maker's own page on 27 September 2026. Trial lengths and editions change; the maker's page is the final word.
Deep Learning with TensorFlow and Keras at a glance
Deep Learning with TensorFlow and Keras is a free, self-paced online course from EDWartens for engineers, developers and students applying AI to real work. It has 13 modules and 21h 42m of video lessons by codebasics, freeCodeCamp.org, Edje Electronics and others, with written notes and worked problems, a practical project with a document pack and a 15-question final assessment (pass mark 60%). Learning is free with an account; an optional certificate with a public verification code is issued when you pass. Last updated 27 September 2026.
All course factsHide course facts
- Price
- Free, for good. No trial, no card. The only paid item is the optional certificate, a small one-off fee.
- Who it is for
- Engineers, developers and students applying AI to real work
- Format
- 13 self-paced modules, 21h 42m of video, written notes, a practice task per module and one final assessment.
- Level
- Intermediate. Intermediate. Needs the Machine Learning with Python course, or equivalent: train/test discipline, metrics, Pandas.
- Brand
- Vendor-neutral
- Software
- Google Colab with a free GPU runtime. TensorFlow and Keras are pre-installed.
- Hardware
- None. A Raspberry Pi is optional for the deployment module.
- Certificate
- Optional EDWartens Certificate of Completion, verifiable by code. Not a vendor credential.
- Video lessons by
- codebasics, freeCodeCamp.org, Edje Electronics, Connor Shorten (independent creators, credited below)
- Language
- English
- Last updated
- 27 September 2026
A shareable EDWartens certificate
Finish every module and pass the final assessment, and the optional EDWartens certificate is yours. It carries a unique verification code on a public page anyone can check, so it stands up when a recruiter looks it up. See it below.
The course itself stays free whether or not you ever buy one.
Stuck? Ask a practising engineer
A free course usually means a comment section and hope. This one does not. Every module has an Ask-your-trainer panel that reaches the same engineers who teach our paid programme: people who commission panels for a living, not moderators.
Pairs well with
PLC programming · FreeSiemens TIA PortalFrom zero electrical knowledge to a working, simulated S7-1200 program, for nothing.
PLC programming · FreeSiemens TIA Portal in Three HoursThe first three hours of the Siemens TIA Portal course, cut to end on a win: what a PLC is, how it is wired, a project configured in TIA Portal, and your first ladder program running in simulation. Finish it in an evening or two, earn a certificate, and carry straight on into the full course.
PLC programming · FreeTIA Portal: Build a MachineOne machine, start to finish. Take a bottle filling line from a written specification and an I/O list to a structured S7-1200 program with a fill station, a capper, a reject sorter and an operator screen with alarms, then test it against a written record and archive it for hand-over. The lessons are the reference; the machine is yours, and it is what you submit.
Instrumentation · FreeInstrumentation for PLC EngineersThe half of the loop that is not code. Follow one measurement from the transmitter in the field, down the 4-20 mA loop, into the analog card, through NORM_X and SCALE_X into engineering units, out again to a valve, and back to the control room when the reading is wrong.More free courses: Free AI courses for engineers
Learning paths with this course
- Applied AI engineer · 4 coursesThe 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.
Learner reviews
No reviews yet
Reviews here are written only by learners who have finished every module of Deep Learning with TensorFlow and Keras, and they are published exactly as written. Finish the course and yours will be the first.
Common questions
Do I need a GPU?
Google Colab gives you one free; every model in the course trains in minutes on it. Set the runtime type to GPU in the first cell of each notebook.
How much maths?
You see gradient descent and backpropagation once, in twenty lines of NumPy, so you know what the framework does. After that Keras does the calculus and you do the engineering. Nothing in the assessments asks for derivatives.
What are the projects in the Deep Learning with TensorFlow and Keras course?
A surface-defect classifier on 1,200 images — baseline CNN, augmentation and transfer learning compared on one sealed test set with per-class recall, Grad-CAM and a TensorFlow Lite export — and a vibration-window classifier comparing a 1-D CNN with an LSTM, plus an autoencoder anomaly score trained on healthy data only.
Should I do this before or after the OpenCV course?
After, ideally: the OpenCV course teaches you what a convolution filter is by making you tune one by hand, and it shows when classical vision is enough. This course is for when it is not.
Is the Deep Learning with TensorFlow and Keras course really free?
Yes. Every module, practice task, project and assessment. You create an account so your progress is saved and the assessments can be marked. The certificate is the only paid item, and only if you want it.
What certificate does the Deep Learning with TensorFlow and Keras course give?
An EDWartens Certificate of Completion, issued when you have finished the modules and passed the final assessment (the project is optional practice), with a verification code anyone can check. It is not a vendor credential and is never described as one.
Who made the video lessons in the Deep Learning with TensorFlow and Keras course?
The creators named in the Credits block at the foot of this page, on their own YouTube channels. EDWartens did not make the videos and the creators are not affiliated with EDWartens. What EDWartens wrote is the study plan, the notes, the practice tasks, the projects and the assessments.
What you walk away with
Your certificate for Deep Learning with TensorFlow and Keras
Finish the course, pass the final, and this is the document with your name on it.
Verifiable by anyone
Adds to LinkedIn in one click
QR code on the certificate
Names what you can do
A permanent link
Earned, not attended
Learning is free. The certificate is optional.
Add it now and pay only when you have finished the course, or come back for it later. One-off, US$28.99, with a receipt.
Issued by EDWartens, the training division of Wartens, as a Certificate of Completion for this self-paced course. Sold by Wartens Ltd (England and Wales). It is not a vendor certification, a university award or a CPD-accredited activity, and it does not certify competence on live equipment. Delivered electronically; see the refund policy.
Credits
Who made the video lessons
The video lessons in this course were created by the people below, not by EDWartens. Every lesson streams from its creator's own YouTube channel; EDWartens neither hosts nor sells that footage, and the creators are not affiliated with EDWartens and do not endorse this course. What EDWartens wrote is the study plan, the notes, the practice tasks and the assessments.
- codebasicsthe Deep Learning with TensorFlow 2.0, Keras and Python series
- freeCodeCamp.orgthe TensorFlow 2.0 complete course and the deep-learning crash course
- Edje Electronicsrunning TensorFlow Lite on a Raspberry Pi
- Connor Shortena lesson in "Evaluating and explaining a deep model"
If you are one of these creators and would like a lesson removed or credited differently, write to info@wartens.com.