Opening soonAI, ML and data scienceIntermediate

Machine Learning with Python, taught live in Hinglish

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.

Join the waitlist Book a free demo class

At a glance

Level: Intermediate
Duration: About 14 weeks · 5–6 hours a week
Syllabus: 8 modules, 3 capstone ideas
Format: Live online, small batch (max 30)
Language: Classes in Hinglish; code and notes in English
Fee: Announced when the batch opens

Tools

Python
NumPy
Pandas
Matplotlib
scikit-learn
XGBoost
LightGBM
Jupyter
Git

Who it's for

Is this course right for you?

College students who want to start machine learning.
Students from class 11–12 with good Python and maths interest.
Graduates and job seekers building an ML project portfolio.
Programmers who want to move into data and AI work.

Before you start

What you need

Comfortable Python: functions, lists, dictionaries and reading files. See our Python programming course.
Basic school maths. We revise what we need.
A laptop or desktop with a stable internet connection

Outcomes

What you'll be able to do.

Real skills you can show — every project goes on your GitHub.

Explain supervised, unsupervised and reinforcement learning with simple examples.
Prepare data with Pandas and NumPy: cleaning, encoding and scaling.
Train regression, tree-based, KNN and SVM models with scikit-learn.
Measure models with accuracy, precision, recall, F1, ROC-AUC and cross-validation.
Use clustering and PCA to explore data without labels.
Tune models and use boosting libraries such as XGBoost and LightGBM.

Syllabus

Machine Learning with Python syllabus, module by module.

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
ML foundations
Understand what machine learning is and set up a clean workflow.
- Supervised, unsupervised and reinforcement learning
- NumPy, Pandas and Matplotlib refresher for ML
- Train, validation and test data
- Overfitting and underfitting
PythonNumPyPandasMatplotlib
Practice: Split a dataset the right way and plot its features
Module 2
Data preprocessing and feature engineering
Turn raw data into clean features a model can use.
- Missing values and outliers
- Encoding categories
- Scaling and normalising features
- Creating new features from old ones
- Pipelines that avoid data leakage
scikit-learnPandas
Practice: Build a reusable preprocessing pipeline
Module 3
Supervised learning: regression
Predict numbers and understand what the model learned.
- Linear regression and gradient descent idea
- Regularisation: Ridge and Lasso
- Error measures: MAE, RMSE, R-squared
scikit-learn
Practice: Predict house prices from simple features
Module 4
Supervised learning: classification
Predict categories and choose the right metric for the problem.
- Logistic regression
- Decision trees and random forests
- K-nearest neighbours and support vector machines
- Confusion matrix, precision, recall, F1 and ROC-AUC
- Imbalanced data and why accuracy can mislead
scikit-learn
Practice: Classify customers who may leave a service
Module 5
Unsupervised learning
Find groups and structure in data that has no labels.
- K-Means, DBSCAN and hierarchical clustering
- Choosing the number of clusters
- PCA for dimensionality reduction
- t-SNE for visualising high-dimensional data
scikit-learn
Practice: Customer segmentation
Module 6
Ensembles and tuning
Improve models in a careful, repeatable way.
- Bagging and random forests
- Boosting: gradient boosting, XGBoost and LightGBM
- Cross-validation done correctly
- Grid search and random search for hyperparameters
XGBoostLightGBMscikit-learn
Practice: Tune a boosting model and compare it with a baseline
Module 7
Responsible ML and saving models
Check a model for fairness and privacy problems and save it for use.
- Bias in training data and how it shows up in results
- Privacy: handling personal data with care
- Explaining predictions with feature importance
- Saving models with joblib and a tiny prediction app
joblibStreamlit
Practice: Wrap one model in a small web app
Module 8
ML projects
Complete two portfolio projects from raw data to a written result.
- Framing the problem and choosing a metric
- Baseline first, then improve
- Writing a clear README and notebook
- Presenting results in plain language
GitGitHub
Practice: House price prediction · Customer segmentation · Fraud detection system

Projects

Capstone projects you can build.

Pick one, plan it like a real team with tickets, build it, and present it. It becomes the centrepiece of your portfolio.

House price prediction
Predict prices from a public dataset, compare several models and explain the main drivers.
Customer segmentation
Group shoppers by behaviour with clustering and describe each group in plain words.
Fraud detection system
Handle very imbalanced data, pick the right metrics and show the trade-off between missed and false alerts.

Opening soon

Be first in the Machine Learning with Python batch.

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

Machine learning engineer (junior)
Data scientist (junior)
Data analyst
AI developer

No course can guarantee a job. We help you build real skills, real projects and a portfolio you can show.

FAQ

Machine Learning with Python: your questions.

When does the batch start?

The batch is not scheduled yet. Join the waitlist and we will tell you first when dates are fixed.

Do I need to know Python before joining?

Yes. You should be comfortable with Python basics. If not, join our Python programming course first.

Should I do data science or machine learning first?

Data science with Python is a good first step because it teaches data cleaning, charts and statistics. This course goes deeper into building models.

Is the maths very hard?

We use school-level maths and explain ideas with code and pictures first. Deeper maths is added only where it helps.

How is this different from free YouTube videos?

You get live classes, small batches, a mentor who reviews your assignments and projects on your GitHub. Videos alone cannot tell you where your code went wrong.

Do I need a laptop?

Yes, a laptop or desktop with internet. Phones and Android tablets are not enough. You do not need a GPU for this course.

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Machine Learning with Python, taught live in Hinglish

Is this course right for you?

What you need

What you'll be able to do.

Machine Learning with Python syllabus, module by module.

ML foundations

Data preprocessing and feature engineering

Supervised learning: regression

Supervised learning: classification

Unsupervised learning

Ensembles and tuning

Responsible ML and saving models

ML projects

Capstone projects you can build.

House price prediction

Customer segmentation

Fraud detection system

Be first in the Machine Learning with Python batch.

Machine Learning with Python: your questions.

Related courses.

Start with one free class.

Machine Learning with Python, taught live in Hinglish

Is this course right for you?

What you need

What you'll be able to do.

Machine Learning with Python syllabus, module by module.

ML foundations

Data preprocessing and feature engineering

Supervised learning: regression

Supervised learning: classification

Unsupervised learning

Ensembles and tuning

Responsible ML and saving models

ML projects

Capstone projects you can build.

House price prediction

Customer segmentation

Fraud detection system

Be first in the Machine Learning with Python batch.

Machine Learning with Python: your questions.

Related courses.

Start with one free class.