Students and self-taught learners who can write simple Python and want a first complete data and machine-learning project.
Beginner level
Data and Machine Learning
From a Spreadsheet to Your First Model
Learn Python for data, clean and explore a real dataset, and train and honestly evaluate your first machine-learning model, finishing with a short report of your own.
- 40hours of work
- 19parts
- Checkedcapstone
- Lifetimeaccess
- Python
- pandas
- Data cleaning
- scikit-learn
- Model evaluation
- Data storytelling
At a glance
Python 3.10 or newer with pip, and either Jupyter or a code editor. You should know variables, loops, functions and lists in Python. No statistics background is needed.
Pass the quizzes, complete the labs and submit your capstone. Everything is checked as you go and your certificate is issued the moment you finish.
What you will be able to do
- 01Load, clean and summarise a dataset with pandas
- 02Ask questions of data with grouping, filtering and simple charts
- 03Split data properly and train a first model with scikit-learn
- 04Choose and read the right metric, and spot overfitting and data leakage
- 05Explain what you found in a clear short report
Curriculum 40 hours in 19 parts
Select a kind to highlight its parts.
Stage 1Clean and explore a dataset7 parts · 11 h
- 1What data science and machine learning areLesson · Short, focused reading with questions to check yourself.1 hFree
- 2Python and pandas for dataLesson · Short, focused reading with questions to check yourself.1 h
- 3Pandas on a real datasetWorkshop · Follow along and build, with the steps and the traps written out.2 h
- 4Cleaning and exploring dataLesson · Short, focused reading with questions to check yourself.1 h
- 5Clean a messy datasetWorkshop · Follow along and build, with the steps and the traps written out.2 h
- 6Check yourself: data foundationsQuiz · Twelve questions, marked at once, with the reason behind each answer.1 h
- 7Clean and explore a datasetLab · Hands-on work. Many labs are checked automatically against what must exist.3 h
Stage 2Train and honestly evaluate a model6 parts · 10 h
- 8Your first model with scikit-learnLesson · Short, focused reading with questions to check yourself.1 h
- 9A classification project, step by stepWorkshop · Follow along and build, with the steps and the traps written out.2 h
- 10Evaluating honestly: overfitting and leakageLesson · Short, focused reading with questions to check yourself.1 h
- 11Regression, tuning and honest comparisonWorkshop · Follow along and build, with the steps and the traps written out.2 h
- 12Check yourself: modelling and evaluationQuiz · Twelve questions, marked at once, with the reason behind each answer.1 h
- 13Train and honestly evaluate a modelLab · Hands-on work. Many labs are checked automatically against what must exist.3 h
Stage 3A reproducible end-to-end pipeline4 parts · 8 h
- 14Visualisation and data storytellingWorkshop · Follow along and build, with the steps and the traps written out.2 h
- 15SQL and other data sources for analystsWorkshop · Follow along and build, with the steps and the traps written out.2 h
- 16Check yourself: data in practiceQuiz · Twelve questions, marked at once, with the reason behind each answer.1 h
- 17A reproducible end-to-end pipelineLab · Hands-on work. Many labs are checked automatically against what must exist.3 h
Stage 4Fairness checks and a model card1 part · 3 h
- 18Fairness checks and a model cardLab · Hands-on work. Many labs are checked automatically against what must exist.3 h
FinishCapstone project1 part · 8 h
- 19A data project and reportCapstone · A full project for your portfolio.8 h
What you take away
An internship certificate with your skills on it.
Made for you automatically when you finish the last part, it names the internship programme, the level and the 40 hours, lists what you showed, and carries a number and a QR code. Anyone can check it on the NIBVEDA verification page, and it shows as withdrawn there if it is ever revoked.
- Proof you completed all 40 hours of this internship programme
- An Internship badge for your profile, CV or portfolio
- Not a job, a stipend or an academic degree, and it says so
Keep building.
3 programmes in this track, from beginner to expert. Buy the learning path and save 15%.
Go beyond your first model. Engineer features, validate without fooling yourself, handle imbalanced data and time series, explain what a model learned, and track experiments reproducibly, finishing with a predictive-model report reviewed by the NIBVEDA team.
- Feature engineering
- Cross-validation
- Gradient boosting
Train neural networks, work with text, then take a model into production: serve it, test it, monitor it for drift, schedule data pipelines, design experiments properly and review risks, finishing with an end-to-end ML system reviewed by the NIBVEDA team.
- PyTorch
- NLP
- Model serving
See all 3 programmes in Data and Machine Learning Take the placement test
Quick answers.
The policies have the full detail.
Is this a real internship?
It is a virtual internship programme: a structured, self-paced course built around a real project, with checked assessments and a certificate. It is not employment, it does not carry a stipend and it is not an academic or AICTE-approved qualification. What it gives you is verified, project-based evidence of skills.
How does self-paced work?
You start when you like and move at your own speed. Each part unlocks when you finish the one before it. There are no live classes and no deadlines. Your access is shown on each programme page.
How is my work checked?
Quizzes are marked as soon as you submit them. Labs use a tick-list of tasks. Intermediate and Expert programmes finish with a capstone that the NIBVEDA team reviews and either accepts or sends back with comments.
Can I get a refund?
Please read the refund policy before you buy. It explains when a refund is possible.
Ready to start?
Rs. 411.82 including GST. Lifetime access. Read the first lesson free before you decide.