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Sabaqاردو

Practical Machine Learning in Urdu (Data Science with Dr. Wasim)

By Data Science with Dr. Wasim

Course audio
Course audio: Urdu
Level
Beginner
Length
88 lessons · 16.7 hours

What you'll be able to do

  • Prepare data with NumPy and pandas for modelling
  • Build and assess classifiers with scikit-learn pipelines
  • Train regression models with gradient descent and regularisation
  • Group data with K-means and choose the value of K

About this course

An 88-lesson, roughly 17-hour Urdu course that alternates short theory videos with numbered Python labs. After the intuition and types of machine learning it warms up with Python, NumPy, pandas and visualisation, then covers overfitting, parametric and non-parametric models, KNN, decision trees, Naive Bayes, scikit-learn pipelines, model assessment, linear and logistic regression with gradient descent, ridge regularisation, SVMs, neural networks for classification and finally K-means clustering. The steady theory-then-lab rhythm makes it a good fit for beginners who learn best by coding along.

Syllabus

88 lessons · 16:39:13 in total

  1. 11- The Intution behind Machine Learning14:02
  2. 22 Examples of ML Models14:19
  3. 33 Formal Definition and Applications of ML11:13
  4. 44 Types of Machine Learning8:56
  5. 55 Theoretical Concepts - I10:46
  6. 66 Theoretical Concepts - II16:21
  7. 77 - Lab 1 - Python Warmup (Requires Anaconda)13:32
  8. 88 - Lab 2 - Data Types and Casting8:16
  9. 99 - Lab 3 - Data Structures in Python19:05
  10. 1010 - Lab 4 - Functions in Python10:53
  11. 1111- Lab 5 - Intro to NumPy10:02
  12. 1212 - Lab 6 - Working with Numpy I19:33
  13. 1313 - Lab 7 - Working with NumPy - II9:58
  14. 1414 - Lab 8 - Pandas Basics21:51
  15. 1515 - Lab 9 - Loading Data with Pandas13:32
  16. 1616 - Lab 10 - Data Visualization I15:32
  17. 1717 - Lab 11 - Data Visualization II16:38
  18. 1818- What is Overfitting9:27
  19. 1919 - Parametric vs Non Parametric Models4:10
  20. 2020 - KNN Intuition29:41
  21. 2121 - KNN for Categorical Features8:52
  22. 2222 - KNN - Final Remarks7:41
  23. 2323 - Lab 12 - KNN Demo I19:28
  24. 2424 - Lab 13 - KNN Demo II13:15
  25. 2525 - Decision Tree Intuition10:23
  26. 2626 - Decision Tree - Working Example16:48
  27. 2727 - Decision Trees - Final Remarks3:45
  28. 2828 - Random Forest - Intuition4:11
  29. 2929 - Naïve Bayes - Intuition13:16
  30. 3030 - Naïve Bayes Hypothesis5:39
Show all 88 lessons
  1. 3131 - Naïve Bayes - Final Remarks4:31
  2. 3232 - Lab 14 - Decision Tree and Random Forest Demo20:51
  3. 3333 - Lab 15 - Naïve Bayes Demo10:55
  4. 3434 - Lab 16 - Comparison Demo8:17
  5. 3535 - Multinomial NB - I16:25
  6. 3636 - Multinomial NB - II13:24
  7. 3737 - Lab 17 - Multinomial NB Demo - I17:19
  8. 3838 - Lab 18 Multinomial NB Demo - II13:31
  9. 3939 - Lab 19 - Pipelines in scikit learn7:38
  10. 4040 - Bias Variance trade-off - I10:13
  11. 4141- Bias Variance trade-off - II9:30
  12. 4242- Bias Variance trade-off - III13:44
  13. 4343- Classification Model Assessment12:50
  14. 4444 - Lab 20 - Getting Model Assessment Right in Scikit-Learn6:30
  15. 4545 - Cross Validation6:45
  16. 4646 - Lab 21 - Cross Validation Demo10:06
  17. 4747- Linear Regression Intuition - I7:36
  18. 4848 Linear Regression Intuition - II11:11
  19. 4949 Linear Regression Intuition - III11:53
  20. 5050- Linear Regression Intuition - IV8:32
  21. 5151- Gradient Descent Algorithm11:13
  22. 5252 - Feature Scaling and Polynomial Regression4:02
  23. 5353- Regression Model Assessment5:46
  24. 5454- Lab 22- Linear Regression Demo I9:50
  25. 5555- Lab 23- Linear Regression Demo II8:27
  26. 5656 - Lab 24 - Regression Model Evaluation Essentials7:52
  27. 5757 - Overfitting in Linear Regression12:42
  28. 5858 - Logistic Regression - Intuition14:37
  29. 5959 - Logistic Regression Cost and Overfitting11:05
  30. 6060- Multiclass Classification3:12
  31. 6161 - Sensitivity and Specificity6:35
  32. 6262 - ROC Curve - AUC Score9:06
  33. 6363 - Lab 25 - Ridge Regression (L2 Regularization)13:47
  34. 6464 - Lab 26 - Lasso Regression (L1 Regularization)9:23
  35. 6565 - Lab 27 - Logistic Regression Demo10:24
  36. 6666- Lab 28 - ROC Curve, AUC Score10:39
  37. 6767 - Support Vector Machine Intuition8:47
  38. 6868- SVM: The Kernel Trick10:37
  39. 6969 - SVM: Final Remarks4:18
  40. 70How to Build SVM Models with Scikit-Learn5:56
  41. 71Practical SVM Implementation for Images9:24
  42. 72Building a Text Classifier with SVM6:00
  43. 7373- Neural Network Intuition - 113:19
  44. 7474- Neural Networks Intuition - 217:20
  45. 7575 - How do Neural Networks Learn9:19
  46. 7676- Neural Networks - Final Remarks7:50
  47. 7777- PyTorch Tensors 101: From Zero to Tensor Operations9:27
  48. 78PyTorch Neural Networks: Training a Pretrained Model29:05
  49. 7979- Lab 34- NN for Classification - 18:00
  50. 8080- Lab 35- NN for Classification - 218:35
  51. 8181 - Lab 36 - Dataset and DataLoader Classes10:33
  52. 8282- Lab 37- Multiclass Classification - 113:18
  53. 8383- Lab 38- Multiclass Classification - 219:11
  54. 8484- Unsupervised Learning4:19
  55. 8585- K-means Clustering5:49
  56. 8686- Random Initialization Trap5:26
  57. 8787- Choosing the Value of K7:56
  58. 8888- Lab 39 - Clustering and Choosing the K Value23:18

What to take next

Videos by Data Science with Dr. Wasim, played from their official YouTube channel. Subscribe on YouTube

Official upload by the teacher or institution on their own channel, shown with YouTube's embedded player.

Lessons listed from the official YouTube playlist. Full playlist on YouTube

Lessons last checked Oct 2026. Course summary and lesson structure by Sabaq.

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