تعلم الآلة بأسلوب عملي: رحلة مع كتاب Hands-On Machine Learning Using Scikit-Learn, Keras & TensorFlow - M. ElHosseini - Official Website

Welcome to my academic space—explore, learn, and connect through research, teaching, and innovation in AI and Smart Systems.

Welcome to my academic space—explore, learn, and connect through research, teaching, and innovation in AI and Smart Systems.

تعلم الآلة بأسلوب عملي: رحلة مع كتاب Hands-On Machine Learning Using Scikit-Learn, Keras & TensorFlow

83 Videos

Lecture 01: الخطوة الأولى في الذكاء الصناعي: مقدمة في تعلم الآلة

25:03

Lecture 02: Big Picture | Navigating the Landscape of Machine Learning

17:59

Lecture 03: Unveiling the Giants: Exploring Machine Learning in Facebook, Amazon, and Google

15:49

Lecture 04: Machine Learning Workflow Simplified

13:08

Lecture 05: Supervised ML - Classification & Regression in Practice

11:55

Lecture 06: Exploring Unsupervised Learning: From Clustering to Anomaly Detection

15:02

Lecture 07: Semi-Supervised & Self-Supervised Learning - Enhancing ML with Less Labels

14:19

Lecture 08: Exploring Reinforcement Learning within Unsupervised ML Paradigms

13:22

Lecture 09: Batch Learning vs. Online Learning

18:59

Lecture 10: Instance-Based vs. Model-Based Learning in AI

9:44

Lecture 11: Mastering Data Quality: Overcoming Bias and Noise in ML

17:07

Lecture 12: Mastering Data Quality: Tackling Irrelevant and Missing Data in Machine Learning

13:13

Lecture 13: Mastering Data Quality - Overcoming Overfitting in Machine Learning Models

12:04

Lecture 14: Understanding and Addressing Underfitting

12:48

Lecture 15: Hyperparameters, Validation, and Model Selection

29:05

Lecture 16: Launch Your Machine Learning Project: From Problem to Data 🚀🏠

22:20

Lecture 17: Mastering Google Colab

25:00

Lecture 18: Demystifying Your Data: A Quick Dive into California Housing Prices with Data Structures

24:54

Lecture 19: The Art of the Test Set: Protect Your Models from Data Snooping

17:18

Lecture 20: Essentials of Data Handling in ML| Splitting, Sampling, and Transformations

19:21

Lecture 21: Data Detective: Find the Story in Your Data

17:03

Lecture 22: Missing Data Mastery: Cleaning and Imputation for Better Models

19:48

Lecture 23: Text & Categorical Data Preparation for Real-World ML

13:29

Lecture 24: 📏 Feature Scaling & Transformation: Taming Your Data for ML Success 🚀

22:36

Lecture 25: Mastering Scikit-learn: Design for Machine Learning Success 🚀

8:52

Lecture 26: Beyond Pre-Built: Crafting Custom Transformers for Machine Learning Excellence

12:30

Lecture 27: Transformation Pipelines Masterclass 🚀- Pipelines, ColumnTransformer, and More!

19:38

Lecture 28: Select & Train Your Model 🧠

20:44

Lecture 29: Hyperparameter Power-Up: Master Grid Search for Optimal Models 🚀

17:38

Lecture 30: Randomized Search for Hyperparameter Optimization 🔎

24:17

Lecture 31: The Building Blocks of Sampling Distributions 🧱

24:47

Lecture 32: Hyperparameter | From Prior Knowledge to Adaptive Sampling: Expert tactics 🧠

14:49

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

12:01

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

17:40

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

14:55

Lecture 36: Binary Classification through MNIST Dataset

16:19

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

19:33

Lecture 38: Confusion Matrix

29:47

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

15:23

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

21:46

Lecture 41: Receiver Operating Characteristic (ROC) curve

25:15

Lecture 42: Multiclass (Multinomial) Classification

23:29

Lecture 43: Error Analysis

24:28

Lecture 44: MultiLabel Classification

12:56

Lecture 45: Multioutput Classification

9:48

Lecture 46: Normal Equation | Linear Regression

46:40

Lecture 47: Simple Trick | Linear Regression

35:43

Lecture 48: Square - Absolute Trick | Linear Regression

25:13

Lecture 49: Error Functions - Healthcare Application | Linear Regression

19:38

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

39:58

Lecture 51: Polynomial Regression

27:27

Lecture 52: Cross Validation - Learning Curves

39:47

Lecture 53: Introduction to Regularization

30:11

Lecture 54: Ridge Regularization

26:40

Lecture 55: Lasso & Elastic Net Regularization

26:12

Lecture 56: Early Stopping

17:29

Lecture 57: Perceptron Basics: Understanding Linear Classifiers

1:11:01

Lecture 58: Error Functions and the Perceptron Trick

29:27

Lecture 59: Perceptron Algorithm Implementation

30:28

Lecture 60: Perceptron Applications - Spam Email Filter

22:12

Lecture 61: Logistic Regression

45:52

Lecture 62: Logistic Regression Applications [Iris - Diabetes]

25:42

Lecture 63: Softmax Regression

36:42

Lecture 64: Softmax Regression By Hand | Implementation

27:00

Lecture 65: 🚀 Support Vector Classifier (SVC)

24:29

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

26:30

Lecture 67: 🚀 Kernel Trick

12:55

Lecture 68: Polynomial Kernel | SVM

24:33

Lecture 69: Radial Basis Function (RBF) | SVM

39:04

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

31:56

Lecture 71: 📚 Decision Tree Part 01 - Introduction 🌳

26:13

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

30:20

Lecture 73: Mastering the Gini Impurity Index 🎯📊

19:29

Lecture 74: Demystifying Entropy in Decision Trees 🔥🌳

24:43

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

14:12

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

14:51

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

21:56

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

16:07

Lecture 79: Hard Voting & Soft Voting | Ensemble Learning

20:56

Lecture 80: Bagging & Pasting 🤝✨ | Ensemble Learning

20:49

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

13:26

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

28:08

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

36:25

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