Progress from Python and statistics into reproducible machine-learning workflows
AI & Data ScienceIntermediate
Data Science and Machine Learning Professional
Move from Python and statistics to evaluated machine-learning models and a job-ready portfolio.
✦
Portfolio outcomePrice prediction system with regression and error analysis
Duration14 weeks
Live lessons56
LanguageEnglish & Tamil
Learning modeLive + lab
Program fee₹19999
The real outcome
What you'll actually be able to do.
A mentor-led 14 weeks program designed around practical labs, guided assignments, portfolio work, and clear progress reviews. Learn the workflow employers and modern teams use, then apply it in projects you can confidently explain.
01
Prepare data for modelling
02
Train supervised and unsupervised models
03
Evaluate and improve model performance
04
Communicate data science findings
Course benefits
Why this learning path matters.
Each benefit is connected to practice, projects, and work you can demonstrate.
Learn data preparation, feature engineering, modelling, evaluation, and communication
Understand why a model works, where it fails, and how to compare alternatives
Produce notebooks, reports, and model cards suitable for a technical portfolio
Your learning roadmap
From “new to this” to “I built this.”
56 live lessons across 4 focused modules.
Module 01Python and statistics
+
PythonPandasStatistics
Module 02Machine learning
+
RegressionClassificationClustering
Module 03Model building
+
Feature engineeringEvaluationTuning
Module 04ML capstone
+
NotebookTechnical reportPresentation
AI project studio
Five projects. Built for your portfolio.
Progress from focused practice to one complete, presentation-ready capstone.
Price prediction system with regression and error analysis
PlanBuildReview
Customer segmentation engine using clustering
PlanBuildReview
Customer churn classification and intervention model
PlanBuildReview
Personalized recommendation system prototype
PlanBuildReview
Deployed machine-learning capstone with model card and monitoring plan
PlanBuildReview
Your stack
Tools you'll get comfortable with.
PythonPandasNumPyscikit-learnJupyterGitHub
Job opportunities
Where these skills can take you.
These are realistic roles to explore as your portfolio and experience grow.
Junior Data ScientistMachine Learning AnalystJunior ML EngineerApplied AI InternData Analyst — Python Track
Hiring readiness
How this curriculum helps you compete in 2026.
Hiring teams look for evidence that you can apply skills, explain decisions, and work through realistic constraints.
Evaluated ML projects prove more than library familiarity
Documented notebooks show reproducibility, reasoning, and data-cleaning discipline
Model comparison and error analysis prepare learners for technical questioning
Capstone presentation connects model metrics with real product or business outcomes
Job titles and hiring requirements vary by company and location. Completing a course strengthens skills and portfolio evidence; it does not guarantee employment.
Is this your track?
This course fits if you are…
- ✓Developers moving into ML
- ✓Analytics learners
- ✓Technical graduates
Good to know
Before you start.
- ✓Basic Python preferred
- ✓High-school mathematics basics
ZZiawinElite Institute
Digital credential
Certificate of achievement
This certifies thatStudent Name
has successfully completed the career programData Science and Machine Learning Professional
PythonPandasNumPy
Sample credential IDZE · 2026 · DATA-SCIENCE-MACHINE-LEARNING-PROFESSIONAL
✦AIREADY