Practical Machine Learning in Urdu (Free YouTube Course)

This course covers complete contents covered in an undergraduate program. It covers the course's theoretical and practical (in Python) aspects.

Prerequisites: You should have prior programming experience in any popular programming language such as C++, C#, or Java.

The playlist is available here.

The following topics are covered in this course:

1. Introduction to Machine Learning

• The Intuition Behind Machine Learning
• Examples of ML Models
• Formal Definition and Applications of ML
• Types of Machine Learning
• Theoratical Concepts - I
• Theoratical Concepts - II
• Lab 1- Python Warmup
• Lab 2- Data Types and Casting
• Lab 3- Data Structures in Python
• Lab 4- Functions in Python

2. Core ML Libraries (NumPy, Pandas, Matplotlib, Seaborn)

• Lab 5- Intro to Numpy
• Lab 6- Working with NumPy - I
• Lab 7- Working with NumPy - II
• Lab 8- Pandas Basics
• Lab 9- Loading Data with Pandas
• Lab 10- Data Visualization - I
• Lab 11- Data Visualization - II

3. KNN Algorithm

• What is Overfitting?
• Parametric vs Non-parametric Models
• KNN Intuition
• KNN for Categorical Features
• KNN - Final Remarks
• Lab 12- KNN Demo - I
• Lab 13 - KNN Demo - II

4. Decision Tree

• Decision Tree - Intuition
• Decision Tree with a working example
• Decision Tree - Final Remarks
• Random Forest - Intuition
• Lab 14- Decision Tree and Random Forest Demo

5. Naive Bayes Classifier

• Naive Bayes Intuition
• Naive Bayes Hypothesis
• Naive Bayes Final Remarks
• Lab 15- Naive Bayes Demo
• Lab 16- Comparison Demo
• Multinomial Naive Bayes - I
• Multinomial Naive Bayes - II
• Lab 17- Multinomial NB Demo - I
• Lab 18- Multinomial NB Demo - II
• Lab 19- Pipelines in sci-kit learn

6. Bias Variance Tradeoff, Cross-Validation, and Classification Assessment

• Bias Variance Tradeoff- I
• Bias Variance Tradeoff- II
• Bias Variance Tradeoff- III
• Classification Model Assessment
• Lab 20- Classification Model Assessment
• Cross Validation
• Lab 21- Cross Validation Demo

7- Linear and Polynomial Regression with Regularization

• Linear Regression Intuition - I
• Linear Regression Intuition - II
• Linear Regression Intuition - III
• Linear Regression Intuition - IV
• Gradient Descent Algorithm
• Feature Scaling and Polynomial Regression
• Regression Model Assessment
• Lab 22- Linear Regression Demo - I
• Lab 23- Linear Regression Demo - II
• Lab 24- Regression Models Evaluation
• Overfitting in Linear Regression
• Lab 25- Ridge Regression (L2 Regularization)
• Lab 26- Lasso Regression (L1 Regularization)

8- Logistic Regression, ROC, and AUC

• Logistic Regression - Intuition
• Logisitic Regression Cost Function and Overfitting
• Multiclass Classification
• Sensitivity and Specificity
• ROC Curve and AUC Score
• Lab 27- Logistic Regression Demo
• Lab 28- ROC Curve and AUC Score Demo

9- Support Vector Machine (SVM)

• Support Vector Machine - Intuition
• SVM: The kernel trick
• SVM: Final Remarks
• Lab 29- SVM - Demo I
• Lab 30- SMV for Digit Classification - Demo II
• Lab 31- SVM for Text Classification - Demo III

10- Multilayer Perceptron and Neural Networks

• Neural Network Intuition - 1
• Neural Network Intuition - 2
• How do NNs Learn
• Neural Networks - Final Remarks
• Lab 32- Pytorch: Tensors
• Lab 33- Defining and Training a NN
• Lab 34- NN for Classification - 1
• Lab 35- NN for Classification - 2
• Lab 36- Dataset and DataLoader Classes
• Lab 37- Multiclass Classification for MNIST Fashion - 1
• Lab 38- Multiclass Classification for MNIST Fashion - 2

11- Unsupervised Learning (Clustering)

• Unsupervised Learning
• K-means Clustering
• Random Initialization Trap
• Choosing the Value of K
• Lab 39- Clustering and Choosing the Value of K

Good Luck! :)