Machine Learning

Ezitech Institute · India

FULL TIME

Job Description

Notebooks are where models are born and where most of them die. You will take one all the way to a served endpoint that other people call.

# fit and validate
model. fit (X_train, y_train)
score = evaluate (model, X_val)

Validation ROC-AUC 0.871

Ends as

A served endpoint

Python
NumPy
MLflow
FastAPI
Python
NumPy
MLflow
FastAPI
Python
NumPy
MLflow
FastAPI
Python
NumPy
MLflow
FastAPI
Python
NumPy
MLflow
FastAPI
Python
NumPy
MLflow
FastAPI
Python
NumPy
MLflow
FastAPI
Python
NumPy
MLflow
FastAPI
Python
NumPy
MLflow
FastAPI

12 Weeks

25 hrs Per week

1 Deployed model

The work

The model is the easy part

Fitting a classifier takes four lines. Cleaning the data, choosing an honest metric, avoiding leakage and shipping it so someone else can call it. That is the twelve weeks.

1 You get a real, messy dataset and a business question
2 You build a baseline before touching anything fancy
3 Your mentor challenges your metric and your leakage
4 You serve the model and monitor it in production

Python basics are covered in weeks 1 to 2. You do not need prior ML.

src/train.py

# Week 6: leakage-safe pipeline, honest validationfrom sklearn.pipeline import Pipelinefrom sklearn.model_selection import StratifiedKFold, cross_val_scorepipe = Pipeline([ ("prep", preprocessor), # fit inside each fold ("clf", HistGradientBoostingClassifier( learning_rate=0.06, max_depth=None, early_stopping=True, random_state=42)),])cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)scores = cross_val_score(pipe, X_train, y_train, scoring="roc_auc", cv=cv, n_jobs=-1)print(f"ROC-AUC {scores.mean():.3f} +/- {scores.std():.3f}")

Skills

Eight things you will actually be able to do

pandas, NumPy and writing analysis code that someone else can rerun.

Data cleaning

Missing values, outliers, leakage, encoding and the 70% of the job nobody films.

Exploratory analysis

Distributions, correlations and finding the signal before modelling it.

Core algorithms

Regression, trees, ensembles and clustering: how they work, not just how to call them.

Transformations, pipelines and building features without leaking the target.

Honest evaluation

Cross-validation, the right metric for the problem, and class imbalance.

Tuning

Hyperparameter search, regularisation and knowing when to stop.

Serving models

Packaging, a FastAPI endpoint, versioning and monitoring drift.

The 12-week plan

Four phases. Each one ends in a deliverable that is reviewed and graded before the next opens.

Python and data

The toolkit before the models.

Python fundamentals, environments and notebooks
NumPy, vectorisation and why loops are slow
Git for data projects and reproducible runs

A cleaned dataset with a documented exploratory analysis and stated data quality issues.

The capstone

Every intern ships a model someone can call

A cleaned dataset and reproducible pipeline
An honest evaluation, not a cherry-picked score
A served endpoint other systems can call

Three things that make this different from a course you watch on a screen.

Live client briefs

You work on real Ezitech projects with real deadlines and real revision rounds, not recycled practice exercises.

A mentor, not a playlist

Weekly reviews with someone who does this work for a living, plus a team of interns around you to build with.

Portfolio and experience letter

You finish with shipped work you are allowed to show and a letter that says exactly what you delivered.

And you leave with

Verified certificate

Issued by Ezitech Institute and verifiable by employers.

On company letterhead, stating exactly what you delivered.

A trained model served behind an API, with an evaluation report.

CV rewrite, portfolio case study and a technical mock interview.

A route into the team

Top performers are considered first for paid junior roles.

Confidence in review

Twelve weeks of defending your own work to a senior practitioner.

Built for you if

You are an undergraduate or fresh graduate in CS, IT, maths or stats
You are comfortable with basic programming and school-level mathsYou want to move past tutorials into real data
You need a substantial final-year project

Not a fit if

You want a certificate without doing the work
You cannot give 25 hours a week for 12 weeks
You are looking for a purely theoretical course
You want to skip review and work alone

How much maths do I need for machine learning? Less than people fear, more than zero. Comfort with algebra, basic probability and reading a formula is enough. The track teaches the intuition behind each algorithm alongside the code. You will not be deriving proofs.

How many hours a week does it take? A minimum of 25 hours across 12 weeks, split between onsite sessions, mentor reviews and independent work. Interns who treat it as full-time finish with noticeably stronger portfolios.

Is it remote or onsite? Hybrid. Onsite days run at the Ezitech office in Rawalpindi; the rest of the week is remote with scheduled check-ins. Candidates outside Rawalpindi and Islamabad can complete the track fully remotely if they attend the weekly review calls.

Do I need a GPU or a powerful laptop? Not for this track. Classical machine learning on tabular data runs fine on any modern laptop, and heavier runs use Google Colab or the Ezitech workstation during onsite days.

Can final-year students apply? Yes, and many do. The capstone is regularly used as a final-year project because it is built from a real brief with real requirements. Agree the scope with your mentor in week one if your university has a set submission format.

Three months, one shipped piece of real work, a mentor who reviews you every week.

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Company	Ezitech Institute
Location	India
Type	FULL TIME
Niche	tech

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