Machine Learning Internship
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.
model.fit(X_train, y_train)
score = evaluate(model, X_val)
- Python
- scikit-learn
- pandas
- NumPy
- MLflow
- FastAPI
- Python
- scikit-learn
- pandas
- NumPy
- MLflow
- FastAPI
- Python
- scikit-learn
- pandas
- NumPy
- MLflow
- FastAPI
- Python
- scikit-learn
- pandas
- NumPy
- MLflow
- FastAPI
- Python
- scikit-learn
- pandas
- NumPy
- MLflow
- FastAPI
- Python
- scikit-learn
- pandas
- NumPy
- MLflow
- FastAPI
- Python
- scikit-learn
- pandas
- NumPy
- MLflow
- FastAPI
- Python
- scikit-learn
- pandas
- NumPy
- MLflow
- FastAPI
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.
- 1You get a real, messy dataset and a business question
- 2You build a baseline before touching anything fancy
- 3Your mentor challenges your metric and your leakage
- 4You serve the model and monitor it in production
Python basics are covered in weeks 1 to 2. You do not need prior ML.
# Week 6: leakage-safe pipeline, honest validation from sklearn.pipeline import Pipeline from sklearn.model_selection import StratifiedKFold, cross_val_score pipe = 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}")
Eight things you will actually be able to do
Python for data
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.
Feature engineering
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
- pandas: loading, joining, reshaping, grouping
- 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.
Modelling
Baselines first, then earn every improvement.
- Regression and classification fundamentals
- Train/validation/test splits and cross-validation
- Decision trees, random forests and boosting
- Choosing metrics that match the business question
A baseline plus a tuned model, with a written comparison of what actually improved the score.
Real-world problems
The things that break models in production.
- Data leakage detection and prevention
- Class imbalance, resampling and threshold tuning
- Feature pipelines that transform consistently
- Model interpretability with SHAP and permutation importance
A leakage-audited pipeline with an interpretability report explaining what drives the predictions.
Ship it
From notebook to endpoint.
- Packaging the pipeline and versioning artefacts
- Serving predictions with FastAPI
- Monitoring drift, latency and error rates
- Portfolio case study, CV rewrite, mock interview
A deployed prediction endpoint, an evaluation report and a public repository.
Every intern ships a model someone can call
You take a real Ezitech dataset (churn, demand, classification of support tickets), and carry it from raw CSV to a served endpoint with an honest evaluation report.
- A cleaned dataset and reproducible pipeline
- An honest evaluation, not a cherry-picked score
- A served endpoint other systems can call
What every Ezitech intern gets
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.
Verified certificate
Issued by Ezitech Institute and verifiable by employers.
Experience letter
On company letterhead, stating exactly what you delivered.
A deployed model
A trained model served behind an API, with an evaluation report.
Interview preparation
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 maths
- You 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
Questions people ask
Everything applicants email us about, answered before you apply.
How much maths do I need for machine learning?
How many hours a week does it take?
Is it remote or onsite?
Do I need a GPU or a powerful laptop?
Can final-year students apply?
How do I apply?
Not sure Machine Learning is the one?
Every AI internship runs three months, hybrid, with the same mentor model. Or take the six-month All-in-One flagship.
Deep Learning
Neural networks trained, tuned and served on real problems.
View trackComputer Vision
Detection, tracking and OCR running on real video, not sample clips.
View trackData Science
From messy data to a decision someone actually makes.
View trackBig Data
Pipelines, warehouses and datasets too large for a laptop.
View trackTake a model past the notebook
Three months, one shipped piece of real work, a mentor who reviews you every week. Registration takes five minutes.
Ezitech Institute · Rawalpindi, Pakistan · info@ezitech.org · +92 345 5555396