Data Science & AI/ML Course in Île de Nantes
For Île de Nantes learners, this course goes from raw data to a deployed model — not just a notebook exercise.
- Live cohorts, not recordings
- Practitioner-taught
- Community & placement
- Lifetime access
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For Île de Nantes analysts and engineers
Live sessions cover Python, statistics, ML models and MLOps basics, with a capstone project you can show in interviews. This course goes from raw data to a deployed model for Île de Nantes students — not just a notebook exercise.
Curriculum breakdown
Module 1: Python and statistics foundations
Foundational skills Île de Nantes students need before touching a real model — Python fluency and core statistics.
Topics covered
- Python for data analysis (pandas, numpy)
- Probability and statistical inference
Module 2: Data wrangling and EDA
Cleaning and exploring real, messy data for Île de Nantes students — the part of the job that takes most of the actual time.
Module 3: Machine learning models
Building, validating and comparing models on real datasets.
Topics covered
- Classification and regression models
- Model evaluation and validation
Module 4: MLOps and deployment basics
Getting a model from a notebook into something running in production.
How the cohort runs
Format and schedule
Live, capstone-driven cohort
Live sessions for Île de Nantes students, building toward a capstone project from raw data to a deployed model.
Who this course is for
Analysts moving into data science
Île de Nantes analysts ready to move from reporting into predictive modelling.
Engineers adding ML to their toolkit
Île de Nantes engineers who want to add machine learning skills to an existing technical background.
Outcomes and support
Capstone project
A full project from raw data to a deployed model, built by Île de Nantes students during the cohort.
Tools used
Python, pandas, scikit-learn, SQL, plus an intro to PyTorch
The same tools used in real production data science work.
Note
Basic statistics helps, but the course builds up the concepts Île de Nantes students need as they go.
Why Île de Nantes students choose Deepak Suhag
Taught by a practitioner who ships models to production for clients, not just an academic curriculum.
How it works
Simple, transparent process — from first contact to measurable results.
Enrol & Onboard
Instant portal access, cohort Slack invite, and full session calendar on day one.
Live Sessions
Weekly Zoom sessions with real campaign walkthroughs, live dashboard reviews, and Q&A.
Build & Get Feedback
Hands-on assignments on your own campaigns with direct 1:1 feedback from Deepak.
Graduate & Network
Industry certificate, alumni community, job board access, and ongoing placement support.
Tools & platforms
The exact stack I use daily across growth marketing, web development, AI, and automation — no guesswork, no vendor lock-in.
Why work with Deepak
Here's what makes this different from every other option in Île de Nantes.
Taught by a practitioner
Every module comes from live campaigns with real budgets — not textbook theory or outdated slides.
Live cohorts, not recordings
Ask questions in real time, get live feedback on your campaigns, and learn with a cohort of peers.
Practitioner-led curriculum
Real ad accounts, real case studies, real budgets — everything relevant to where you work, wherever that is.
Career-ready outcomes
Portfolio projects, alumni Slack, and direct referrals to companies actively hiring in your city.
Everything you need to know
Still have a question that isn't answered here? Reach out directly — I respond to every inquiry personally.
Ask a question01Do I need a math background to join from Île de Nantes?
Basic statistics helps, but the course builds up the concepts you need as you go.
02What tools does the course use?
Python, pandas, scikit-learn, and SQL, plus an intro to PyTorch.
03Is this different from the Data Analytics course?
Yes — this covers predictive modelling and ML; Data Analytics focuses on SQL, dashboards and reporting.
04Is there a capstone project for Île de Nantes students?
Yes — a full project from raw data to a deployed model.
I started teaching because I was frustrated seeing marketers memorise theory they'd never use. Every lesson I teach comes from a live campaign, a real mistake, or a real win. You'll leave with skills you can use tomorrow morning.