D24
Advanced SpecializationData & AIAdvanced10 weeks
Machine Learning
Machine Learning is a 10-week advanced course for Python-comfortable learners, teaching supervised and unsupervised learning, model tuning, and deployment basics using scikit-learn, XGBoost, and FastAPI. Learners build classification, regression, and clustering models, then complete an end-to-end capstone project that takes raw data to a deployed, monitored prediction API.
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
Enrollment isn't open for this course yet — check back soon or contact us.
Course Curriculum
A module-by-module breakdown of what you'll cover in Machine Learning.
01Getting Started & Environment Setup
4 topicsWeeks 1–2
- 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
02Basic
3 topicsWeek 3
- Descriptive statistics and probability distributions for ML
- pandas for ML data preparation
- Setting up a reproducible ML environment
03Intermediate
3 topicsWeeks 4–5
- Linear and logistic regression
- Decision trees and support vector machines
- Train/validation/test splits and evaluation metrics
04Advanced
4 topicsWeeks 6–7
- Random forests and gradient boosting (XGBoost/LightGBM)
- Hyperparameter tuning and cross-validation
- Clustering, PCA, and feature engineering
- Anomaly detection
05Pro
4 topicsWeek 8
- SHAP, LIME, and AutoML basics
- Serving a model via a REST API
- Model versioning, drift monitoring, and CI/CD for ML
- Final Capstone Project
06AI Pro
4 topicsWeeks 9–10
- Using Claude, ChatGPT, and Perplexity for ML pipeline development
- Diagnosing model and deployment issues with AI
- Verifying AI-suggested pipelines for data leakage
- Project: AI-Assisted Machine Learning Development Workflow
Tools & Technologies
Hands-on practice with the same tools used by working professionals.
sciscikit-learn
XGBXGBoost
OptOptuna
SHASHAP
MLfMLflow
WeiWeights & Biases
FasFastAPI & Docker
AutAutoGluon
Real-Time, Industry-Based Projects
This is project-based learning modeled on real industry scenarios — you'll build 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.
Career & Placement Support
100% of students enrolled in Machine Learning get full access to placement support — real tools and guidance, not just a promise. This is support and access, not a guaranteed job outcome.
Resume Builder
A resume auto-built from your real completed projects and certificates, ready to export.
Shareable Portfolio
A public portfolio page showcasing your approved projects, with a link you can share with employers.
Interview Preparation
Guidance on presenting your projects and experience clearly in real interviews.
Peer & Mentor Community
Ongoing access to our learner community for questions, feedback, and support after you finish.
What Your Certificates Look Like
Real, verifiable certificates with your name, Machine Learning, and a QR code anyone can scan to confirm they're genuine — one for completing the course, and a separate one if you take the internship track.
Course Completion Certificate
SAMPLE
Data24ZoneCertificate of Completion
Illustrative recipient
Your Name Here
Example certificate for completion of
Machine Learning
- Certificate No.
- D24-SAMPLE
- Mode
- Self-Paced
- Duration
- 10 weeks
- Example issue date
- On completion
Sample only — not a real, issued certificate.
Verification is available only on issued certificates.
Internship Completion Certificate
SAMPLE
Data24ZoneCertificate of Internship
Illustrative recipient
Your Name Here
Example certificate for completion of
Machine Learning
- Certificate No.
- D24-SAMPLE
- Mode
- Internship
- Duration
- 10 weeks
- Example issue date
- On completion
Sample only — not a real, issued certificate.
Verification is available only on issued certificates.
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, so it assumes some prior familiarity with the subject. It starts with a "Getting Started & Environment Setup" module covering tool installation before the main curriculum.
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?
Internship (₹2,499) — Work through 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 — the Internship track means working through real projects with our team and earning a separate Internship Certificate.
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 your internship projects are approved. 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.
Is placement support included with Machine Learning?
Yes — every enrolled student gets full access to our placement support: resume building, a shareable project portfolio, mock interview preparation, and guidance applying what you've built to real job applications. This is support and access, not a guaranteed job outcome — how quickly it leads to an offer depends on the market and your own effort.
