6-Month Program · Supervised & Unsupervised Learning with Python
Machine Learning — Basics
32 hands-on modules · 20 tools & libraries · 180+ hours of training · An introduction to supervised/unsupervised learning models using Python, for those moving toward AI roles. Covers regression, classification, scikit-learn and model evaluation, built around real datasets from day one.
Who this course is for
Built for graduates, working professionals and analysts who want to move into AI/ML and data science roles, and are ready to learn Python-based machine learning from the ground up.
By the end you'll build and evaluate regression and classification models confidently with scikit-learn, apply unsupervised learning techniques like clustering and PCA, and take a project from raw data to a deployment-ready model.
1Program structure
Four phases over 24 weeks — from Python and data foundations, through regression and classification, to unsupervised learning and a full capstone project.
| Phase | Modules | Focus |
|---|---|---|
| Phase 1 — Python & Data Foundations for ML | 01–08 | Python, NumPy, Pandas, data cleaning, EDA and the end-to-end ML workflow |
| Phase 2 — Supervised Learning: Regression | 09–16 | scikit-learn, linear/polynomial regression, regularization and regression model evaluation |
| Phase 3 — Classification & Model Evaluation | 17–24 | Classification algorithms, ensembles, evaluation metrics and hyperparameter tuning |
| Phase 4 — Unsupervised Learning & Capstone | 25–32 | Clustering, dimensionality reduction, basic deployment and a full capstone ML project |
2Full curriculum — all 32 modules
Click any phase to expand its modules, or open a module directly for a detailed description.
Phase 1Python & Data Foundations for MLModules 01–08Python, NumPy, Pandas, data cleaning, EDA and the end-to-end ML workflow+
01Python Refresher for Data Science+
Core Python syntax, data structures and control flow, refreshed with a data-science lens.
02NumPy for Numerical Computing+
Arrays, vectorized operations and the numerical foundation every ML library builds on.
03Pandas for Data Manipulation+
Loading, filtering, grouping and reshaping tabular data with Pandas DataFrames.
04Data Cleaning & Preprocessing+
Handling missing values, duplicates and inconsistent data before it reaches a model.
05Exploratory Data Analysis (EDA)+
Systematically exploring a new dataset to understand distributions, relationships and red flags.
06Data Visualization with Matplotlib & Seaborn+
Building the charts that reveal patterns and communicate findings during EDA and reporting.
07Feature Engineering Basics+
Creating, transforming and selecting the features that actually help a model learn.
08Introduction to Machine Learning & the ML Workflow+
Supervised vs unsupervised learning, and the standard workflow from raw data to a trained model.
Phase 2Supervised Learning: RegressionModules 09–16scikit-learn, linear/polynomial regression, regularization and regression model evaluation+
09Introduction to scikit-learn+
The scikit-learn API pattern — fit, predict and transform — used across almost every model.
10Simple & Multiple Linear Regression+
Predicting a continuous outcome from one or more input features with linear regression.
11Polynomial Regression & Regularization (Ridge, Lasso)+
Modeling non-linear relationships, and controlling overfitting with Ridge and Lasso regularization.
12Train-Test Split & Cross-Validation+
Splitting data correctly and using cross-validation to get a trustworthy read on model performance.
13Evaluating Regression Models (RMSE, MAE, R²)+
Reading and interpreting the core metrics used to judge how well a regression model performs.
14Feature Scaling & Encoding Categorical Data+
Standardization, normalization and encoding techniques that make raw data model-ready.
15Handling Outliers & Missing Data in ML Pipelines+
Detecting outliers and building preprocessing pipelines that handle messy real-world data.
16Regression Project: End-to-End Price Prediction Model+
Building a complete regression model from raw data to evaluated predictions on a pricing dataset.
Phase 3Classification & Model EvaluationModules 17–24Classification algorithms, ensembles, evaluation metrics and hyperparameter tuning+
17Logistic Regression for Classification+
Predicting categories rather than numbers, starting with the workhorse logistic regression model.
18K-Nearest Neighbors (KNN)+
Classifying new data points based on the labels of their closest neighbors.
19Decision Trees+
Building interpretable, rule-based classifiers, and understanding how splits are chosen.
20Random Forests & Ensemble Basics+
Combining many trees into a stronger, more stable model, and the basic idea behind ensembling.
21Support Vector Machines (SVM)+
Finding the optimal boundary between classes, including the kernel trick for non-linear data.
22Naive Bayes Classifier+
A fast, probability-based classifier that works especially well for text and categorical data.
23Model Evaluation: Confusion Matrix, Precision, Recall, F1, ROC-AUC+
Reading beyond accuracy to judge a classifier properly, especially on imbalanced data.
24Hyperparameter Tuning (GridSearchCV, RandomizedSearchCV)+
Systematically searching for the model settings that perform best on your data.
Phase 4Unsupervised Learning & CapstoneModules 25–32Clustering, dimensionality reduction, basic deployment and a full capstone ML project+
25Introduction to Unsupervised Learning+
Finding structure in data with no labels, and where unsupervised techniques fit in practice.
26K-Means Clustering+
Grouping similar data points into clusters, and choosing a sensible number of clusters.
27Hierarchical Clustering+
Building nested clusters and reading dendrograms to understand data structure at multiple levels.
28Dimensionality Reduction with PCA+
Reducing a large number of features down to the ones that matter most, using Principal Component Analysis.
29Anomaly Detection Basics+
Spotting unusual data points using simple statistical and model-based anomaly detection techniques.
30Model Deployment Basics+
Saving a trained model and exposing it through a simple API so others can use its predictions.
31Building an End-to-End ML Pipeline+
Chaining preprocessing, training and evaluation into a single, reusable, production-style pipeline.
32Capstone: Full ML Project (Problem to Deployment-Ready Model)+
Take a real business problem from raw data through EDA, modeling and evaluation to a deployment-ready model, and present it.
320 tools & libraries you'll use
Everything is taught on real, industry-standard Python libraries you'll use later on the job — not simulations. Swipe or use the arrows to browse every category.
4How each module is taught
Every module follows the same practical, real-data lens, so you always know what to expect in class.
Concept Walkthrough
Trainer explains the concept with a real-world problem first, then the algorithm or technique, using a real or realistic dataset.
Live Along
You build and train the same model step by step, right alongside the trainer.
Hands-on Exercise
An independent task applying the concept to a new dataset or business question.
Doubt Clearing
One-on-one help for anyone stuck, before the class moves to the next module.
Weekly Assessment
A short practical ML coding test every week so gaps are caught early, not at the end.
5Projects & portfolio
Six practical, portfolio-ready projects across the full syllabus — the kind of work you'll show in interviews.
- Project 1. EDA and data-cleaning project on a real, messy dataset
- Project 2. End-to-end regression model predicting a continuous outcome (e.g., price prediction)
- Project 3. Classification model with full evaluation (e.g., customer churn prediction)
- Project 4. Model comparison & tuning project across multiple algorithms with cross-validation
- Project 5. Unsupervised learning project: customer segmentation with clustering and PCA
- Project 6. Capstone: end-to-end ML pipeline from raw data to a deployment-ready model, presented as you would to a manager
6Assessment & certification
Weekly practical assessments covering Python, regression, classification and model evaluation, with instructor review and feedback.
Final practical exam across all four phases, plus the capstone project used as your demonstrable, portfolio-ready proof of skill.
Certificate of completion, plus resume and ML-project portfolio support through our 100% Placement Assist program.
7Quick answers
Do I need to know Python before joining?
No prior Python experience is required. Module 1 is a Python refresher built specifically for data science, and the pace assumes you're new to it.
Do I need a strong background in maths or statistics?
Basic comfort with numbers is enough. We cover the statistics and maths concepts you need exactly when a technique requires them — this isn't a pure theory or research-maths course.
Will I actually build and train models, or is it mostly theory?
You'll write code and train models from the early modules onward. Every module includes hands-on practice with scikit-learn on real or realistic datasets, ending in a full capstone project.
Do I need my own laptop for this course?
A laptop is strongly recommended since you'll be coding daily and building a project portfolio, but institute computers with Python and the required libraries are also available in class.
Are classes online, offline, or both?
Both. Offline batches run at our Alpha-1, Greater Noida campus; online live batches follow the same curriculum with the same trainers. Morning, evening and weekend timings are available.
What can I do after completing this course?
You'll be ready for junior data scientist, ML engineer and AI-adjacent analyst roles, with a project portfolio of real models, and can progress into advanced AI/deep-learning tracks with the same placement support.
Ready to see a class live?
Book a free, no-obligation demo class and sit in on a real Machine Learning session before you decide anything.