32:47

📊 Regression: From Data Points to Real-World Predictions

33:08

📊 Scatter Plots & Correlation | فهم العلاقات بين المتغيرات الكمية

20:01

📊 Understanding Kurtosis in Data Analysis 📈

25:38

📊 Symmetry, Skewness, and Statistical Measures 📈

46:23

Lecture 05: What Kinds of Patterns Can Be Mined? 🔍📊

31:03

Lecture 04: What Kinds of Data Can Be Mined? 🔍 | Exploring Databases, Data Warehouses & More

21:46

Lecture 03: Knowledge Discovery Process 🚀 | KDD in Action

09:13

Lecture 02: Market Basket Analysis - Example | Data Mining & Data Warehouse

30:23

Lecture 01: Introduction 🚀 | Data Mining & Data Warehouse

36:25

Lecture 83: 🚀 Mastering AdaBoost: A Deep Dive into Boosting with Handy Examples | Ensemble Learning

28:08

Lecture 82: Random Forests: Extra Trees, and Feature Importance Explained! 🌳📧 | Ensemble Learning

13:26

Lecture 81: Out-of-Bag Evaluation & Random Patches | Ensemble Learning

20:49

Lecture 80: Bagging & Pasting 🤝✨ | Ensemble Learning

20:56

Lecture 79: Hard Voting & Soft Voting | Ensemble Learning

16:07

Lecture 78: 🎓 Predicting Graduate Admissions with Decision Trees! 📈

21:56

Lecture 77: 🌳 Decision Trees for Regression Explained with Hands-On Example! 📊

14:51

Lecture 76: How to Split a Decision Tree Based on Continuous Features 🌳📊

14:12

Lecture 75: How to Prevent Decision Trees from Overfitting 🌳🚫

24:43

Lecture 74: Demystifying Entropy in Decision Trees 🔥🌳

19:29

Lecture 73: Mastering the Gini Impurity Index 🎯📊

30:20

Lecture 72: DT vs. Random Forest – Understanding the White Box Model 🌳

26:13

Lecture 71: 📚 Decision Tree Part 01 - Introduction 🌳

31:56

Lecture 70: Radial Basis Function (RBF) - Continued | SVM

39:04

Lecture 69: Radial Basis Function (RBF) | SVM

12:55

Lecture 67: 🚀 Kernel Trick

24:33

Lecture 68: Polynomial Kernel | SVM

26:30

Lecture 66: 🚀 Error function | Support Vector Classifier (SVC)

24:29

Lecture 65: 🚀 Support Vector Classifier (SVC)

27:00

Lecture 64: Softmax Regression By Hand | Implementation

36:42

Lecture 63: Softmax Regression

25:42

Lecture 62: Logistic Regression Applications [Iris - Diabetes]

45:52

Lecture 61: Logistic Regression

22:12

Lecture 60: Perceptron Applications - Spam Email Filter

30:28

Lecture 59: Perceptron Algorithm Implementation

29:27

Lecture 58: Error Functions and the Perceptron Trick

01:11:01

Lecture 57: Perceptron Basics: Understanding Linear Classifiers

17:29

Lecture 56: Early Stopping

26:12

Lecture 55: Lasso & Elastic Net Regularization

26:40

Lecture 54: Ridge Regularization

30:11

Lecture 53: Introduction to Regularization

39:47

Lecture 52: Cross Validation - Learning Curves

27:27

Lecture 51: Polynomial Regression

39:58

Lecture 50: Gradient Descent (Batch - Stochastic - MiniBatch) | Linear Regression

19:38

Lecture 49: Error Functions - Healthcare Application | Linear Regression

25:13

Lecture 48: Square - Absolute Trick | Linear Regression

35:43

Lecture 47: Simple Trick | Linear Regression

46:40

Lecture 46: Normal Equation | Linear Regression

09:48

Lecture 45: Multioutput Classification

12:56

Lecture 44: MultiLabel Classification

24:28

Lecture 43: Error Analysis

23:29

Lecture 42: Multiclass (Multinomial) Classification

25:15

Lecture 41: Receiver Operating Characteristic (ROC) curve

21:46

Lecture 40: 📈 Precision/Recall Curves Decoded: Visualizing the Impact of Threshold Adjustments 📊

15:23

Lecture 39: Precision vs. Recall: Finding the Perfect Balance 📊

29:47

Lecture 38: Confusion Matrix

19:33

Lecture 37: 🎯 Unveiling the Truth: The Limits of Accuracy in Model Evaluation 🔄

16:19

Lecture 36: Binary Classification through MNIST Dataset

14:55

Lecture 35: From Code to Cloud: Mastering System Deployment and Monitoring

17:40

Lecture 34: Confidence Intervals & the Empirical Rule, and Your Pre-Deployment Checklist

12:01

Lecture 33: Beyond the Black Box: Feature Importance & Error Analysis