Overview: Data Science & AI/ML Course in DLF Phase 2

For DLF Phase 2 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 DLF Phase 2 students — not just a notebook exercise.

Curriculum breakdown

Module 1: Python and statistics foundations

Foundational skills DLF Phase 2 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 DLF Phase 2 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 DLF Phase 2 students, building toward a capstone project from raw data to a deployed model.

Who this course is for

Analysts moving into data science

DLF Phase 2 analysts ready to move from reporting into predictive modelling.

Engineers adding ML to their toolkit

DLF Phase 2 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 DLF Phase 2 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 DLF Phase 2 students need as they go.

Quick answer

The Data Science & AI/ML Course in DLF Phase 2 takes you from raw data to a deployed machine learning model, covering Python, statistics, ML models and MLOps basics in one live cohort. It's for DLF Phase 2 analysts and engineers who want predictive modelling skills backed by a real capstone project, and it's worth it if you need proof you can ship a model, not just a notebook full of exploratory charts.

How this compares to a free Kaggle course or ML MOOC

• Ends with a deployed model, not just a notebook with a final accuracy score
• Live instruction and feedback on your own dataset, instead of pre-recorded lectures with an automated grader
• Covers MLOps and deployment basics alongside modelling, which most free courses skip entirely
• Taught by a practitioner who ships models to production for clients, not an academic curriculum written once and left static

Why DLF Phase 2 students choose Deepak Suhag

Taught by a practitioner who ships models to production for clients, not just an academic curriculum.