A live NLP course with Python for students who know the basics of programming and machine learning. You go from cleaning text to classic models, embeddings and transformers with Hugging Face. Small batches, Hinglish classes and a mentor who reviews your work.
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
Clean raw text and prepare it for models.
PythonNLTKspaCyscikit-learn
Practice: Clean and explore a set of news headlines
Module 2
Build working text classifiers without deep learning.
scikit-learnspaCy
Practice: Spam detector
Module 3
Represent word meaning as vectors and use them.
gensimspaCy
Practice: Find similar words and similar documents
Module 4
Model word order with neural networks.
PyTorch
Practice: Small next-word predictor
Module 5
Understand attention and use pre-trained transformer models.
Hugging Face TransformersPyTorch
Practice: Fine-tune DistilBERT on a sentiment dataset
Module 6
Spot bias and privacy risks in text systems.
Practice: Write a short risk note for one of your NLP projects
Module 7
Build and publish two NLP projects with clear results.
StreamlitGitGitHub
Practice: Spam detector · Chatbot · Resume parser
Projects
Pick one, plan it like a real team with tickets, build it, and present it. It becomes the centrepiece of your portfolio.
Classify SMS or email messages as spam, compare TF-IDF models with a transformer, and report errors.
Build a simple question-answering bot for a fixed topic, with clear limits on what it can answer.
Extract names, skills and education from text with spaCy and rules, using only sample or consented data.
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.
Yes, we look at Hindi and other Indian-language text in a few lessons and use multilingual models. Most examples and tools are English-first, and we say clearly where Indian languages are harder.
Yes. LLMs are built on these ideas, and many real tasks still use smaller, cheaper models. Knowing the basics helps you choose the right tool and check the output.
Basic ML helps. If you are new to it, take our Machine Learning with Python course first.
Yes, a laptop or desktop with internet. Heavy training runs on free Google Colab, so a normal laptop is enough.
Keep going
A hands-on machine learning with Python course for students who know Python basics. You learn how models learn from data, build them with scikit-learn and check them honestly. Live classes in small batches, and a mentor reviews every assignment.
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.
A live generative AI and LLM course for students who can already code in Python. You learn how large language models work, use LLM APIs and Hugging Face, and build document Q&A with RAG. Small batches, Hinglish classes and mentor review.
This Python course takes you from your very first line of code to useful programs, games, scripts and data tools. Classes are live, in small batches of up to 30, taught in Hinglish, and a mentor reviews every assignment you push to GitHub. Python is taught inside our Starter and Builder programs.
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.
