Machine Learning Course in Pakistan

This course involves real mathematics, taught from the ground up but unavoidable. Python fluency is a hard prerequisite. For many people Data Analytics is the better route, and that is not a lesser answer.

Apply for this course

Taught, not assumed — but it is real and it is here for a reason.

• Vectors and matrices as data
  Why a dataset is a matrix and what an operation on it means.
• Derivatives, and what "learning" means mechanically hands on
  Gradient descent explained until it stops being a magic word.
• Probability and distributions
  Conditional probability, and why a 99% accurate test can still be usually wrong.
• Loss functions
  How a model is told it is wrong, and why the choice changes what it learns.

Most of the job is not the model.

• Framing a problem as a prediction task
  And recognising when it is not one — a great many business problems are answered better by a query.
• Features, and why they matter more than the algorithm hands on
  Encoding, scaling, and creating the variable that actually carries the signal.
• Train, validation, test — and leakage hands on
  The mistake that produces a brilliant model which fails on day one.
• Baselines hands on
  Always beat "predict the average" before claiming anything.

Regression and classification with scikit-learn.

• Linear and logistic regression hands on
  Starting with the models you can explain to a client.
• Decision trees and random forests hands on
  Where they win, and how they overfit.
• Gradient boosting hands on
  XGBoost and friends — what wins most tabular problems in practice.
• Evaluating honestly hands on
  Precision, recall, ROC — and why accuracy is the wrong measure when 1% of cases are positive.

When there are no labels.

• Clustering and segmentation hands on
  k-means, and how to tell whether the clusters mean anything.
• Dimensionality reduction
  PCA for compression and for seeing structure.
• Working with text hands on
  Tokenising, embeddings, and classifying Urdu and English messages.
• Anomaly detection
  Fraud and fault patterns, and the false-positive cost nobody budgets for.

Enough depth to be useful, honest about what needs a research team.

• From logistic regression to a network hands on
  Layers, activations and backpropagation, built up rather than dropped in.
• Training in PyTorch hands on
  Batches, epochs, overfitting and early stopping.
• Transfer learning hands on
  Fine-tuning an existing model — the only realistic route for most projects here.
• When not to use deep learning
  Small tabular data, and the honest observation that gradient boosting usually wins it.

The step that decides whether the work was worth anything.

• Serving a model as an API hands on
  Wrapping it so an application can call it.
• Drift, and the model that quietly stops working hands on
  Monitoring, retraining, and why yesterday's accuracy is not today's.
• Explaining a prediction
  Feature importance and SHAP — and the legal and ethical weight of "the model decided".
• Bias and harm
  How a model trained on past decisions reproduces past unfairness, and what to check before deploying one that affects people.

A prediction problem, end to end · 5 weeks

From raw data to a served model with an honest evaluation, including a written statement of what it cannot do and where it should not be trusted.

A final assessment marked out of 100; the pass mark is 55, with 120 minutes to complete it.

• Mathematical foundations (20 marks)
• Workflow, leakage and evaluation (30 marks)
• Model selection and implementation (30 marks)
• Deployment, drift and responsible use (20 marks)

Students who pass the final assessment and have project work verified by their trainer receive a completion letter. It states attendance, the assessment result and the projects verified, and carries a code any employer can check at the verification page.

Pakistan's National Vocational & Technical Training Commission publishes a course-contents document for the trades it funds. This programme is mapped to AI, Machine Learning & Deep Learning — 12 weeks, 4 hours a day, 240 contact hours.

Published by NAVTTC and reproduced here so you can check our syllabus against it. We teach it to those hours: 240 contact hours over 12 weeks, the same as the standard. Read the NAVTTC document.

Roles this prepares you for

• Junior ML Engineer
  Uncommon as a first role in Pakistan; usually reached from a developer or analyst position.
• Data Scientist
  Typically expects a degree-level background as well as these skills.
• Analytics Engineer
  The more reachable route, combining this with the data analytics skill set.

What is the fee for the Machine Learning course?

The course fee is PKR 60,000. The fee and syllabus are published on this page before you apply.

How long is the Machine Learning course?

It runs for 3 Months, with 240 contact hours.

Can I join from another city?

On campus in Faisalabad, or live online from anywhere in Pakistan. You can apply online from any city.

Do I need any experience before joining?

The course is pitched at advanced level. Recommended before you start: Python Programming, or equivalent fluency; Comfort with mathematics.

How am I assessed, and what certificate do I get?

There is a final assessment marked out of 100; the pass mark is 55. Students who pass and have project work verified by their trainer receive a completion letter with a verification code that any employer can check.

What jobs can this course lead to?

It is aimed at roles such as Junior ML Engineer, Data Scientist, Analytics Engineer. BvLogic Academy does not guarantee a job, an income or a timeframe.

Interested in this programme?

Apply now, or try the subject free first: there is no reason to pay before you know you like it.