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Machine Learning with Python: Theory to Deployment

Regression, classification, ensembles and neural networks with scikit-learn and PyTorch — on real datasets.

4.7(5,620 ratings) 31,870 learners

Created by Dr. Hassan Ali

  • Updated May 2026
  • Urdu + English
  • Intermediate
  • 52 hours · 296 lectures
  • Certificate included
PKR 3,499PKR 4,99930% off

30% off ends in-- d -- h

30-day money-back guarantee · Secure card payment via Safepay

This course includes

  • 52 hours on-demand video
  • 16 articles & notes
  • 25 downloadable resources
  • 3 quizzes & assignments
  • Access on mobile, laptop and TV
  • Full lifetime access
  • Certificate of completion

What you’ll learn

  • Prepare, clean and engineer features from messy real-world data
  • Train and tune regression, classification and clustering models
  • Evaluate models properly with cross-validation and the right metrics
  • Build neural networks in PyTorch for tabular and image data
  • Deploy a model as an API and monitor it in production
  • Complete three projects: house prices, loan default and crop disease detection

Course content

6 sections · 296 lectures · 52h total length

4 lessons · 1h 19m

  • 13:20
  • 24:45
  • Exploratory data analysis21:10
  • Feature engineering and leakage19:35

5 lessons · 3h 8m

  • Linear regression: property prices26:40
  • Logistic regression and decision boundaries22:15
  • Decision trees and random forests25:30
  • Gradient boosting with XGBoost23:10
  • Project: Loan default prediction90:00

4 lessons · 1h 6m

  • Cross-validation and hyperparameter search20:20
  • Precision, recall, ROC and business costs18:45
  • Explaining models with SHAP16:30
  • Quiz: Pick the right metric10:00

3 lessons · 51 min

  • K-means customer segmentation19:50
  • PCA and dimensionality reduction17:25
  • Anomaly detection basics14:10

4 lessons · 3h 29m

  • Tensors, autograd and training loops27:30
  • CNNs for image classification29:15
  • Transfer learning: crop disease detection32:40
  • Assignment: Improve the baseline120:00

3 lessons · 48 min

  • Serving a model with FastAPI21:05
  • Docker and cloud deployment18:40
  • Monitoring drift in production08:00

Section outlines list the key lessons (10h 41m shown). The full syllabus of 296 lectures unlocks on enrolment.

Requirements

  • Python basics (functions, lists, loops)
  • High-school level algebra; we revise the rest
  • A Google account for free GPU notebooks

Description

This course takes you from 'I've heard of machine learning' to training, evaluating and deploying models with confidence. Each algorithm is introduced with intuition and a hand-drawn diagram, then implemented with scikit-learn on a dataset that actually means something — Lahore and Karachi property prices, microfinance loan repayment, and leaf images from Punjab's wheat fields.

Dr. Hassan covers the maths you need and none you don't. The emphasis is on good habits: train/test splits, leakage, honest evaluation and explaining models to non-technical stakeholders.

Who this course is for

  • Learners who know the basics of Machine Learning and want to reach a professional standard
  • Students and professionals who want to apply AI to real Pakistani business problems
  • Learners who prefer clear explanations in Urdu, with technical terms kept in English
  • Anyone who wants a verifiable certificate to add to their CV or LinkedIn profile

Your instructor

Dr. Hassan Ali

AI Researcher & Associate Professor of Computer Science

Rating
4.9
Learners
128.7k
Courses
3

Dr. Hassan completed his PhD in machine learning and now splits his time between university teaching and consulting for local companies adopting AI. He has supervised over forty final-year projects in NLP and computer vision. His courses favour intuition first, maths second, and working code always.

Machine LearningGenerative AILLM ApplicationsDeep LearningPython

More from Hassan Ali

Learner reviews

4.7

Course rating · 5.6k ratings

Breakdown of 4 written reviews.

Most recent reviews

Bilal Zafar

MS Data Science student, ITU Lahore

Verified learner

The crop disease project is brilliant — local data makes it so much more meaningful than MNIST again.

Helpful?

Nida Hashmi

Data analyst, Islamabad

Verified learner

The evaluation module changed how I think about models at work. Very professional course.

Helpful?

Ali Haider

Software engineer, Lahore

Verified learner

Strong content. The PyTorch part assumes a little more Python than stated, so revise classes first.

Helpful?

Zunaira Iqbal

BSAI student, COMSATS Islamabad

Verified learner

Long but worth it. I'd love an NLP module in a future update.

Helpful?

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