D24
Data & AIAdvanced10 weeks
Machine Learning
Design, train, and deploy machine learning models with hands-on projects covering classification, regression, and clustering.
What you'll learn
Supervised learning: classification and regression
Unsupervised learning and clustering techniques
Model tuning, validation, and deployment basics
An end-to-end ML project from raw data to prediction
Course details
Duration
10 weeks
Best for
Learners comfortable with Python who want to build predictive models
On completion
Verified certificate & placement assistance
Compare enrollment modes
| Mode | Price | What you get |
|---|---|---|
| Live Class | ₹4,999 | Instructor-led live cohort with doubt-clearing sessions — includes both the course and internship certificates. |
| Recorded Batch | ₹2,999 | Full recordings of a completed live batch — includes both the course and internship certificates. |
| Internship | ₹2,499 | Work on 4 real projects with our team and earn an internship certificate (course certificate not included). |
| Self-Paced | ₹1,999 | Learn anytime with the full recorded curriculum and community access. |
Choose how you learn
Course Curriculum
A module-by-module breakdown of what you'll cover in Machine Learning.
01
Getting Started & Environment Setup
Week 1- Installing Python and scikit-learn
- Installing FastAPI and Docker
- Creating free Weights & Biases and AutoGluon environment access
- Verifying your setup with a basic scikit-learn script
02
Math, Statistics & Python Foundations
Weeks 2–3- Linear algebra and probability for ML
- NumPy and pandas for data handling
- Implementing linear regression from scratch
- scikit-learn basics
03
Supervised Learning: Regression & Classification
Week 4- Linear and logistic regression
- K-nearest neighbors and decision trees
- Train/validation/test splits
- Evaluation metrics (F1, RMSE, MAE)
04
Ensemble Methods & Hyperparameter Tuning
Weeks 5–6- Random forests and gradient boosting (XGBoost, LightGBM)
- Bagging vs. boosting
- Grid and Bayesian hyperparameter search
- k-fold cross-validation
05
Unsupervised Learning & Feature Engineering
Week 7- Clustering (K-means, hierarchical, DBSCAN)
- Dimensionality reduction (PCA, t-SNE/UMAP)
- Encoding, scaling, and handling imbalanced data
- ROC curves and model calibration
06
Model Interpretability & AutoML
Weeks 8–9- SHAP and LIME for explainability
- Partial dependence plots and model cards
- AutoML baselines (AutoGluon, H2O)
- Fairness and bias checks
07
MLOps: Deployment & Monitoring
Week 10- Experiment tracking with MLflow / Weights & Biases
- Packaging models with FastAPI and Docker
- Drift detection and monitoring
- Dataset and model versioning
Tools & Technologies
Hands-on practice with the same tools used by working professionals.
sciscikit-learn
XGBXGBoost
OptOptuna
SHASHAP
MLfMLflow
WeiWeights & Biases
FasFastAPI & Docker
AutAutoGluon
Hands-on Projects
This is project-based learning — you'll build real, portfolio-ready work as you go, not just watch lectures.
Credit default risk classifier with SHAP-based explanations for each decision
Customer churn early-warning system producing a ranked retention action list
Retail demand forecasting comparing classical time-series models against gradient boosting
Customer segmentation for marketing using clustering and dimensionality reduction
Deployed, monitored ML model served as a containerized API with drift tracking
Every course includes a guided internship on top of your project work — so you graduate with real experience, not just a certificate.
Communication & Soft Skills
Built into every course, alongside the technical curriculum.
Communication Skills
Practice explaining technical work clearly to teammates, managers, and clients.
Practical AI Skills
Hands-on training in using AI tools well — prompting, everyday workflows, and where AI actually helps versus where it doesn't.
Teamwork & Collaboration
Work in small project teams using real workflows like stand-ups and code reviews.
Problem Solving
Build the habit of breaking down ambiguous problems into clear, solvable steps.
Time Management
Learn to plan, prioritize, and deliver project work against realistic deadlines.
Presentation Skills
Present your project work and results with confidence, as you would to a client.
Resume & Profile Building
Build a strong resume, LinkedIn profile, and project portfolio that recruiters notice.
Interview Preparation
Mock interviews and portfolio reviews to get you ready for real job applications.
Student Reviews
“Solid, practical coverage of the algorithms that actually get used in production, not just toy examples.”
Naveen Raj
Data Engineer
“The projects forced me to think about real deployment concerns, which most courses skip entirely.”
Shruti Pillai
Analytics Lead
Frequently Asked Questions
What will I learn in the Machine Learning course?
You'll learn: Supervised learning: classification and regression; Unsupervised learning and clustering techniques; Model tuning, validation, and deployment basics; An end-to-end ML project from raw data to prediction.
What tools and technologies does the Machine Learning course cover?
This course covers: scikit-learn, XGBoost, Optuna, SHAP, MLflow, Weights & Biases, FastAPI & Docker, AutoGluon.
Is the Machine Learning course beginner-friendly?
This is a advanced-level course. It still starts with a "Getting Started & Environment Setup" module covering tool installation, but assumes some prior familiarity with the subject.
How long does the Machine Learning course take to complete?
The course is designed to be completed in approximately 10 weeks, depending on your pace.
What enrollment options are available for Machine Learning?
Live Class (₹4,999) — Instructor-led live cohort with doubt-clearing sessions — includes both the course and internship certificates. Recorded Batch (₹2,999) — Full recordings of a completed live batch — includes both the course and internship certificates. Internship (₹2,499) — Work on 4 real projects with our team and earn an internship certificate (course certificate not included). Self-Paced (₹1,999) — Learn anytime with the full recorded curriculum and community access.
Is there an internship option for Machine Learning?
Yes — enrolling in the Internship track (or the Live/Recorded tracks, which include it) means working through 4 real projects with our team and earning a separate Internship Certificate, in addition to the course completion certificate for Live/Recorded enrollments.
Will I get a certificate after completing Machine Learning?
Yes. Self-paced enrollment earns a course completion certificate once your final project is reviewed and approved. Internship enrollment earns an internship certificate once all 4 internship projects are approved. Live and Recorded enrollments earn both. Every certificate is publicly verifiable on our website and can be added directly to your LinkedIn profile.
What kind of projects will I build in Machine Learning?
You'll work on real projects such as: Credit default risk classifier with SHAP-based explanations for each decision; Customer churn early-warning system producing a ranked retention action list; Retail demand forecasting comparing classical time-series models against gradient boosting; Customer segmentation for marketing using clustering and dimensionality reduction; Deployed, monitored ML model served as a containerized API with drift tracking.
