Overview: Data Science & AI/ML Course in Kralingen

For Kralingen 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 Kralingen students — not just a notebook exercise.

Curriculum breakdown

Module 1: Python and statistics foundations

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

Who this course is for

Analysts moving into data science

Kralingen analysts ready to move from reporting into predictive modelling.

Engineers adding ML to their toolkit

Kralingen 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 Kralingen 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 Kralingen students need as they go.

Quick answer

The Data Science & AI/ML Course in Kralingen 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 Kralingen 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 Kralingen students choose Deepak Suhag

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