Machine Learning Complete Course in Urdu (Codanics)
By Codanics
- Course audio
- Course audio: Urdu
- Level
- Beginner
- Length
- 97 lessons · 27.6 hours
What you'll be able to do
- Clean a dataset: handle missing values, outliers, scaling and encoding
- Train regression and classification models with scikit-learn
- Compare models with cross-validation and evaluation metrics
- Finish an end-to-end prediction project
About this course
Codanics' long-form machine learning course in Urdu, over a hundred lessons and roughly 28 hours. It starts with what machine learning is, its types and the full path from model building to deployment, then spends real time on data pre-processing: missing values, outliers, inconsistencies, merging, scaling and encoding. The algorithm part covers linear and logistic regression, KNN, SVM, decision trees, random forest, Naive Bayes, regularisation, cross-validation and evaluation metrics, and it ends with a complete heart-disease prediction project. A strong Urdu route for students and job seekers who already know basic Python.
Before you start: Basic Python
Syllabus
97 lessons · 27:35:02 in total
- 1What is machine learning?7:27
- 2Types of Machine Learning15:38
- 3ML Model Building to Deployment | Steps A-Z34:28
- 4What is an algorithm? #codanics8:25
- 5Training and Testing Data | Features and Labels | Model4:19
- 6Overfitting vs. Underfitting in Machine Learning10:15
- 7Important Python libraries for Machine Learning7:29
- 8Installation steps for machine learning environment14:45
- 9Data Pre-processing before ML Model30:29
- 10Steps in Data Pre-processing7:23
- 11Dealing with Missing values in Python45:52
- 12Dealing with Data Inconsistencies / Anomalies25:40
- 13Outliers | Data Pre-processing45:19
- 14Data Merging in Python using Pandas9:04
- 15Data concatenation using pandas in python3:58
- 16Data Preprocessing Steps2:07
- 17Feature Scaling and Normalization21:44
- 18Standard Scaling or standardizing the data10:55
- 19Min-max, MaxAbs, and Robust Scalars in python4:16
- 20Normalization and Nonlinear Data Transformation14:24
- 21L2 and L1 Normalization in python12:37
- 22Feature Scaling vs. Normalization9:03
- 23Tips about Scaling and normalization2:51
- 24Assignment Alert1:55
- 25What is feature encoding? #datapreprocessing13:06
- 26Benefits of feature encoding12:44
- 27Feature Encoding in Python using scikit-learn20:49
- 28Feature-encoding with pandas python4:46
- 29Data Discretization | Data Binning #datapreprocessing17:48
- 30Data preprocessing3:12
Show all 97 lessons
- 31Scikit-learn base Jupyter notebook for ML modeling20:04
- 32Linear Regression Concept and Coding in Python1:03:55
- 33Logistic Regression does classification8:08
- 34Logistic Regression and example in Python A-Z29:10
- 35Evaluation Metrics for Regression & Classification Models8:13
- 36Train test split matters20:32
- 37Support Vector Machines (SVM) Theory23:49
- 38Support Vector Machines (SVM) in Python18:59
- 39K-Nearest Neighbors (KNN) Theory in urdu/hindi21:49
- 40Euclidean Distance19:01
- 41Manhattan Distance9:10
- 42Minkowski Distance8:56
- 43Why Minkowski Distance is Important?3:44
- 44Hamming Distance4:55
- 45K-Nearest Neighbors (KNN) Regressor and Classification in Python20:26
- 46Algorithms we have learned so far7:46
- 47What is Decision tree Algorithm? | Decision Tree (Part-1)9:33
- 48Elements of Decision Tree | Decision Tree (Part-2)12:52
- 49Entropy, Gini impurity and information gain theory | Decision Tree (Part-3)22:09
- 50Entropy, Gini impurity and information gain in python | Decision Tree (Part-4)16:20
- 51Decision Tree Classifier in Python | Decision Tree (Part-5)23:02
- 52Ensemble Algorithms in machine learning21:22
- 53Random Forest | Machine Learning (Part-1)18:55
- 54Random Forest in Python for Classification and Regression | (Part-2)17:25
- 55Evaluation Metrics in Machine Learning4:31
- 56Evaluation Metrics for Regression models19:23
- 57Evaluation Metrics for Classification Algorithms31:00
- 58Ensemble Algorithms Family8:00
- 59Boosting in Ensemble Methods21:29
- 60Boosting Algorithms and their pros. and cons.11:48
- 61Booting algorithms vs. Neural Networks13:30
- 62xgBoost vs. Random forest vs. Decision Tree | in python23:13
- 63catBoost algorithm in Python16:13
- 64Hyperparameter Tuning and Cross Validation18:58
- 65Cross Validation in machine learning (Part-1)12:23
- 66Cross Validation in machine learning (Part-2)2:24
- 67pipeline in Machine Learning using Scikit-learn18:57
- 68Best Model Selection in Python using Scikit-learn9:53
- 69Naive Bayes Algorithm (Part-1)16:18
- 70Naive Bayes Algorithm | Machine Learning-101 complete course | (Day-17)1:01:12
- 71Types of Naive Bayes Algorithm | NB (Part-2)6:18
- 72NAIVE Bayes in Python | Naive Bayes Algorithm (Part-3)7:17
- 73Questions and Answers | Naive Bayes Algorithm (Part-4)1:06
- 74Best hyperparameter tuned Model Selection24:09
- 75Google colab crash course in 26 minutes25:41
- 76How to select the best model (Presentation-1) and Feedback24:11
- 77How to select the best model (Presentation-2) and Feedback26:04
- 78Question and Answers session on 90th day of this course38:31
- 79Encoding and Inverse Transform the data26:47
- 80Basic Method | Missing Values Imputation (Part-1)11:12
- 81Machine Learning models | Missing Values Imputation (Part-2)13:07
- 82Advance Methods | Missing Values Imputation (Part-3)4:01
- 83Supervised Machine Learning8:10
- 84Mathematics and Assumptions of Linear Regression9:35
- 85Polynomial Regression Theory and Assumptions14:10
- 86Polynomial Regression in Python with coding8:18
- 87Ridge Regression in Python L2 Regularization23:46
- 88Lasso Regression | L1 Regularization13:04
- 89Heart Disease prediction | A complete ML Project (Part-1)45:08
- 90Heart Disease prediction | A complete ML Project (Part-2)1:17:16
- 91Heart Disease prediction | A complete ML Project (Part-3)21:27
- 92Heart Disease prediction | A complete ML Project (Part-4)11:04
- 93Heart Disease prediction | A complete ML Project (Part-5)8:26
- 94Heart Disease prediction | A complete ML Project (Part-6)17:07
- 95Heart Disease prediction | A complete ML Project (Part-7)30:24
- 96Machine Learning vs. Deep Learning13:58
- 97Small Data vs. Big Data for Deep Learning2:30
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Videos by Codanics, played from their official YouTube channel. Subscribe on YouTube
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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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