A live deep learning course for students who know Python and basic machine learning. You build neural networks from the idea up, then use both PyTorch and TensorFlow/Keras for images and sequences. Small batches, Hinglish classes and mentor review on every assignment.
At a glance
Tools
Who it's for
Before you start
Outcomes
Real skills you can show — every project goes on your GitHub.
Syllabus
Each module has a clear goal, live classes, a lab session and practice you push to GitHub. Your mentor reviews it and tells you exactly how to improve.
Module 1
Explain how a small network learns, and build one from scratch.
PythonNumPy
Practice: A tiny neural network written with NumPy only
Module 2
Train the same model in two major frameworks and see how they differ.
PyTorchTensorFlowKerasGoogle Colab
Practice: Handwritten digit classifier in both frameworks
Module 3
Classify images with CNNs and reuse existing models.
PyTorchKeras
Practice: Image classifier for a small custom dataset
Module 4
Model data that comes in order, such as text and time series.
PyTorchKeras
Practice: Sentiment classifier · Simple forecasting model
Module 5
Make training stable and reduce overfitting.
PyTorchKeras
Practice: Improve a weak model step by step and log every change
Module 6
Run tidy experiments and understand the limits of deep models.
GitTensorBoard
Practice: Write a short model card for one of your models
Module 7
Finish and present two projects from data to result.
GitGitHub
Practice: Image classifier · Text generator · Forecasting model
Projects
Pick one, plan it like a real team with tickets, build it, and present it. It becomes the centrepiece of your portfolio.
Train a CNN with transfer learning on a small dataset, such as local crop leaves or Indian street signs.
Train a small sequence model to generate text and discuss what it gets wrong.
Predict a time series such as power use or weather, and compare it with a simple baseline.
Opening soon
This specialisation isn't scheduled yet. Join the waitlist and we'll tell you first when a batch opens. We open the courses with the most interest first.
Where it can lead
Roles this skill helps you towards
No course can guarantee a job. We help you build real skills, real projects and a portfolio you can show.
FAQ
The batch is not scheduled yet. Join the waitlist and we will tell you first when dates are fixed.
No. We use Google Colab, which gives free GPU time within limits. You need a laptop or desktop with internet, not a gaming machine.
Both are widely used. Learning the same ideas in two frameworks helps you read most tutorials and project code.
Yes, basic ML helps a lot. Our Machine Learning with Python course is the best step before this one.
We explain gradients and loss with pictures and code first. You need school-level algebra to start.
Keep going
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Baking your code to give you a pie
Join a live demo class, meet the mentor and see how a CodeBakerz batch works. No payment, no pressure — decide after.
