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

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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