Learn from data, then put a model inside an application you can explain. 12 weeks at the Madhapur desk. You leave the data, the evaluation, and the model.
From Python and the dataset through a model in an application.
Data first
Statistics, features, and an evaluation you can show before you ship.
Madhapur desk
Classes at Road No. 45, or hybrid if you cannot sit in.
The path
A model is ready when you can explain the miss.
Machine learning with AI starts with the data you were given. You clean it, choose a model, and write down what the evaluation says. The last weeks put that model in a small application and say where it should not be used.
Walk through the 12-week machine learning path with the Madhapur desk.
Guides practical QA workflows, automation foundations, and career-ready project reviews.
Chandra Hasa Gunisetty, QA Lead
Who it is for
Four seats on a model, from the table to the app.
Data steward
A model cannot fix a table you have not looked at.
What you cover
You prepare Python, the rows, and a picture of what the data actually contains.
Instructor-led labs, then a capstone the team can run.
Two people reviewing a printed data table
In the lab
What you practice on a model.
See the data, train one model, then say what the evaluation does and does not allow.
See the data
Open the table, the missing values, and the picture before you pick a model.
Train one model
Regression or classification, with the features you can defend.
Read the evaluation
The score, the errors, and the slice where the model fails.
Leave it runnable
A small application and a note another person can follow.
Skills you practice
Python
Tables
Statistics
Regression
Classification
Neural nets
Evaluation
A small app
Python
Tables
Statistics
Regression
Classification
Neural nets
Evaluation
A small app
Roadmap
Twelve weeks, in the order a model is earned.
Hybrid · online or at the desk · data, models, evaluation, then an application.
Weeks 1–3
Python and the data. The language, the table, and a picture of it.
Python for the lab
The language you will use to load a table and repeat a check.
A notebook you can rerun
Functions for a repeated check
The table
Rows, missing values, and the columns a model is allowed to see.
What each column means
What you will not use
A picture of the data
Charts and a short statistical read before any model.
What the chart shows
What you still do not know
Weeks 4–7
Models. Regression, classification, and a neural net on one task.
Regression
Predict a number and say how far the misses are.
The target
The error you will report
Classification
Predict a label and look at the cases you got wrong.
The classes
The mistakes, written down
A neural net, one task
A small network when the table is no longer enough, still with an evaluation.
Why this model
The same evaluation as before
Weeks 8–9
Judgment. Compare models and name what they may decide.
Compare
More than one model, on the same split, with the same metric.
The split
The metric you chose
The miss
Where the model fails, and what you will not let it decide.
The failing slice
The limit, written down
Weeks 10–12
The application. A model a teammate can run, then the capstone note.
Out of the notebook
A small application that calls the model you kept.
One input
One prediction someone else can see
Capstone
The data, the model, the evaluation, and how to run it again.
What you kept
How the next person runs it
Data
Write down what the table contains
The lab is ready when you can point at a column and say whether the model may see it.
Models
Keep the model the errors support
You do not keep the first model that runs. You keep the one you can explain.
Application
Leave a way to run it
The note says the input, the prediction, and where the model should not be used.
Capstone
One model, from the table to a note someone else can follow.
In class
Each block ends with something you can rerun.
The weeks are instructor-led. You work in the lab, online or at the desk.
Table
Model
Evaluation
The lab, with the class
Capstone
A model you can walk through
The capstone starts with a dataset and ends with a model inside a small application.
You show the data, the evaluation, the misses, and how another person runs it.
Data
Model
Evaluation
Run note
Two people walking through the finished model
How the weeks run
How the twelve weeks run.
A huddle before the data lab
01
You start with the table.
The first weeks are Python, the rows, and a picture of the data. You do not train until that is familiar.
The class comparing models
02
Then you train and compare.
Regression, classification, and a small neural net stay on the same evaluation.
Pairing on the run note
03
You leave a run note.
The last weeks put the model in a small application and say where it stops.
Certificate
A certificate of completion for this program.
Syncpedia issues a certificate of completion after the 12 weeks and the capstone. It names the program. It does not list a salary, a placement rate, or a job offer.
Syncpedia
Certificate of completionMachine Learning with AIIssued after the 12 weeks and the capstone
Python
Tables
Statistics
Regression
Classification
Neural nets
Evaluation
A small app
