كم عدد دروس كورس 100 Days of Machine Learning؟

الكورس يحتوي على 134 درس بإجمالي مدة 62 ساعة 50 دقيقة.

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كورس 100 Days of Machine Learning - أكاديمية مصر ٢٤

Name: كورس 100 Days of Machine Learning 2026
Availability: InStock
Author: CampusX

What is Machine Learning? | 100 Days of Machine Le

EG-24 ACADEMY

EG24

EG-24ACADEMY

— أكاديمية مصر 24 —

CERTIFICATE

OF COMPLETION

THISCERTIFICATE IS PROUDLY PRESENTED TO

اسمك هنا

FOR SUCCESSFULLY COMPLETING THE COURSE

كورس 100 Days of Machine Learning

ISSUED ON

24 أغسطس 2026

Ahmed Hamdy

AHMED HAMDY

FOUNDER

VERIFY CERTIFICATE

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✅ تتبع تقدمك في الكورس

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📋 محتوى الكورس 134 درس · 62 ساعة 50 دقيقة

1 What is Machine Learning? | 100 Days of Machine Learning 20:00
2 AI Vs ML Vs DL for Beginners in Hindi 16:02
3 Types of Machine Learning for Beginners | Types of Machine learning in Hindi | Types of ML in Depth 27:42
4 Batch Machine Learning | Offline Vs Online Learning | Machine Learning Types 11:28
5 Online Machine Learning | Online Learning | Online Vs Offline Machine Learning 19:28
6 Instance-Based Vs Model-Based Learning | Types of Machine Learning 16:44
7 Challenges in Machine Learning | Problems in Machine Learning 23:40
8 Application of Machine Learning | Real Life Machine Learning Applications 29:02
9 Machine Learning Development Life Cycle | MLDLC in Data Science 25:13
10 Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer | Data Science Job Roles 26:23
11 What are Tensors | Tensor In-depth Explanation | Tensor in Machine Learning 41:29
12 Installing Anaconda For Data Science | Jupyter Notebook for Machine Learning | Google Colab for ML 37:06
13 End to End Toy Project | Day 13 | 100 Days of Machine Learning 30:43
14 How to Frame a Machine Learning Problem | How to plan a Data Science Project Effectively 22:22
15 Working with CSV files | Day 15 | 100 Days of Machine Learning 36:30
16 Working with JSON/SQL | Day 16 | 100 Days of Machine Learning 17:00
17 Fetching Data From an API | Day 17 | 100 Days of Machine Learning 22:50
18 Fetching data using Web Scraping | Day 18 | 100 Days of Machine Learning 37:49
19 Understanding Your Data | Day 19 | 100 Days of Machine Learning 15:23
20 EDA using Univariate Analysis | Day 20 | 100 Days of Machine Learning 30:31
21 EDA using Bivariate and Multivariate Analysis | Day 21 | 100 Days of Machine Learning 38:03
22 Pandas Profiling | Day 22 | 100 Days of Machine Learning 13:04
23 What is Feature Engineering | Day 23 | 100 Days of Machine Learning 24:52
24 Feature Scaling - Standardization | Day 24 | 100 Days of Machine Learning 32:38
25 Feature Scaling - Normalization | MinMaxScaling | MaxAbsScaling | RobustScaling 23:31
26 Encoding Categorical Data | Ordinal Encoding | Label Encoding 19:53
27 One Hot Encoding | Handling Categorical Data | Day 27 | 100 Days of Machine Learning 30:12
28 Column Transformer in Machine Learning | How to use ColumnTransformer in Sklearn 15:41
29 Machine Learning Pipelines A-Z | Day 29 | 100 Days of Machine Learning 45:39
30 Function Transformer | Log Transform | Reciprocal Transform | Square Root Transform 32:13
31 Power Transformer | Box - Cox Transform | Yeo - Johnson Transform 21:28
32 Binning and Binarization | Discretization | Quantile Binning | KMeans Binning 38:25
33 Handling Mixed Variables | Feature Engineering 12:10
34 Handling Date and Time Variables | Day 34 | 100 Days of Machine Learning 14:18
35 Handling Missing Data | Part 1 | Complete Case Analysis 24:54
36 Handling missing data | Numerical Data | Simple Imputer 31:21
37 Handling Missing Categorical Data | Simple Imputer | Most Frequent Imputation | Missing Category Imp 13:34
38 Missing Indicator | Random Sample Imputation | Handling Missing Data Part 4 37:05
39 KNN Imputer | Multivariate Imputation | Handling Missing Data Part 5 24:27
40 Multivariate Imputation by Chained Equations for Missing Value | MICE Algorithm | Iterative Imputer 18:31
41 What are Outliers | Outliers in Machine Learning 17:07
42 Outlier Detection and Removal using Z-score Method | Handling Outliers Part 2 17:46
43 Outlier Detection and Removal using the IQR Method | Handing Outliers Part 3 14:05
44 Outlier Detection using the Percentile Method | Winsorization Technique 16:23
45 Feature Construction | Feature Splitting 12:22
46 Curse of Dimensionality 15:25
47 Principle Component Analysis (PCA) | Part 1 | Geometric Intuition 33:54
48 Principle Component Analysis (PCA) | Part 2 | Problem Formulation and Step by Step Solution 56:17
49 Principle Component Analysis(PCA) | Part 3 | Code Example and Visualization 43:26
50 Simple Linear Regression | Code + Intuition | Simplest Explanation in Hindi 33:36
51 Simple Linear Regression | Mathematical Formulation | Coding from Scratch 53:31
52 Regression Metrics | MSE, MAE & RMSE | R2 Score & Adjusted R2 Score 43:56
53 Multiple Linear Regression | Geometric Intuition & Code 20:57
54 Multiple Linear Regression | Part 2 | Mathematical Formulation From Scratch 48:11
55 Multiple Linear Regression | Part 3 | Code From Scratch 16:01
56 What are the main Assumptions of Linear Regression? | Top 5 Assumptions of Linear Regression 17:38
57 Gradient Descent From Scratch | End to End Gradient Descent | Gradient Descent Animation 1:57:56
58 Batch Gradient Descent with Code Demo | Simple Explanation in Hindi 1:04:49
59 Stochastic Gradient Descent 49:35
60 Mini-Batch Gradient Descent 22:10
61 Polynomial Regression | Machine Learning 26:46
62 Bias Variance Trade-off | Overfitting and Underfitting in Machine Learning 8:05
63 Ridge Regression Part 1 | Geometric Intuition and Code | Regularized Linear Models 19:58
64 Ridge Regression Part 2 | Mathematical Formulation & Code from scratch | Regularized Linear Models 43:41
65 Ridge Regression Part 3 | Gradient Descent | Regularized Linear Models 18:43
66 5 Key Points - Ridge Regression | Part 4 | Regularized Linear Models 30:17
67 Lasso Regression | Intuition and Code Sample | Regularized Linear Models 28:37
68 Why Lasso Regression creates sparsity? 24:30
69 ElasticNet Regression | Intuition and Code Example | Regularized Linear Models 11:41
70 Logistic Regression Part 1 | Perceptron Trick 47:06
71 Logistic Regression Part 2 | Perceptron Trick Code 17:07
72 Logistic Regression Part 3 | Sigmoid Function | 100 Days of ML 40:44
73 Logistic Regression Part 4 | Loss Function | Maximum Likelihood | Binary Cross Entropy 29:03
74 Derivative of Sigmoid Function 5:57
75 Logistic Regression Part 5 | Gradient Descent & Code From Scratch 36:42
76 Accuracy and Confusion Matrix | Type 1 and Type 2 Errors | Classification Metrics Part 1 34:08
77 Precision, Recall and F1 Score | Classification Metrics Part 2 42:42
78 ROC Curve in Machine Learning | ROC-AUC in Machine Learning Simplified | CampusX 1:11:15
79 Softmax Regression || Multinomial Logistic Regression || Logistic Regression Part 6 38:21
80 Polynomial Features in Logistic Regression | Non Linear Logistic Regression | Logistic Regression 7 9:11
81 Logistic Regression Hyperparameters || Logistic Regression Part 8 13:07
82 Naive Bayes Classifier | Part 1 | Conditional Probability 9:26
83 Naive Bayes Classifier | Part 2 | Independent Events in Probability 7:59
84 Naive Bayes Classifier | Part 3 | Mutually Exclusive Events 1:49
85 Naive Bayes Classifier | Part 4 | Bayes Theorem in Probability 4:27
86 Naive Bayes Classifier | Part 5 | Problem based upon Bayes Theorem 9:00
87 Naive Bayes Classifier | Part 6 | Intuition 14:44
88 Naive Bayes Classifier | Part 7 | Mathematics behind Naive Bayes Algorithm 19:09
89 Naive Bayes Classifier | Part 8 | Simple Example Code 16:03
90 Naive Bayes Part 9 | Handling Numerical Data 8:47
91 What is K Nearest Neighbors? | KNN Explained in Hindi | Simple Overview in 1 Video | CampusX 52:01
92 Support Vector Machines | Geometric Intuition 11:46
93 Mathematics of SVM | Support Vector Machines | Hard margin SVM 34:54
94 Mathematics of Support Vector Machine | Soft Margin SVM 14:38
95 Kernel Trick in SVM | Geometric Intuition 6:18
96 Kernel Trick in SVM | Code Example 14:04
97 Decision Trees Geometric Intuition | Entropy | Gini impurity | Information Gain 58:29
98 Decision Trees - Hyperparameters | Overfitting and Underfitting in Decision Trees 27:23
99 Regression Trees | Decision Trees Part 3 35:15
100 Awesome Decision Tree Visualization using dtreeviz library 18:36
101 Introduction to Ensemble Learning | Ensemble Techniques in Machine Learning 37:43
102 Voting Ensemble | Introduction and Core Idea | Part 1 16:30
103 Voting Ensemble | Classification | Voting Classifier | Hard Voting Vs Soft Voting | Part 2 23:50
104 Voting Ensemble | Regression | Part 3 10:57
105 Bagging | Introduction | Part 1 31:13
106 Bagging Ensemble | Part 2 | Bagging Classifiers 22:32
107 Bagging Ensemble | Part 3 | Bagging Regressor 10:55
108 Introduction to Random Forest | Intuition behind the Algorithm 33:55
109 How Random Forest Performs So Well? Bias Variance Trade-Off in Random Forest 12:52
110 Bagging Vs Random Forest | What is the difference between Bagging and Random Forest | Very Important 12:02
111 Random Forest Hyper-parameters 15:17
112 Hyperparameter Tuning Random Forest using GridSearchCV and RandomizedSearchCV | Code Example 11:44
113 OOB Score | Out of Bag Evaluation in Random Forest | Machine Learning 6:45
114 Feature Importance using Random Forest and Decision Trees | How is Feature Importance calculated 27:20
115 How Adaboost Classifier Works? | Geometric Intuition 17:14
116 AdaBoost - A Step by Step Explanation 19:23
117 AdaBoost Algorithm | Code from Scratch 16:27
118 AdaBoost Hyperparameters | GridSearchCV in Adaboost 11:13
119 Bagging Vs Boosting | What is the difference between Bagging and Boosting 6:17
120 Gradient Boosting Explained | How Gradient Boosting Works? 32:49
121 Gradient Boosting Regression Part 2 | Mathematics of Gradient Boosting 56:42
122 Gradient Boosting for Classification | Geometric Intuition | CampusX 1:04:33
123 Introduction to XGBOOST | Machine Learning | CampusX 1:19:37
124 XGBoost for Regression | XGBoost Part 2 | CampusX 47:17
125 XGBoost For Classification | How XGBoost works on Classification Problems | CampusX 39:08
126 The Maths Behind XGBoost | Machine Learning | CampusX 1:57:28
127 Stacking and Blending Ensembles 35:20
128 K-Means Clustering Algorithm | Geometric Intuition | Clustering | Unsupervised Learning 23:58
129 K-Means Clustering Algorithm in Python | Practical Example | Student Clustering Example | sklearn 10:13
130 K-Means Clustering Algorithm From Scratch In Python | ML Algorithms From Scratch 33:53
131 Agglomerative Hierarchical Clustering | Python Code Example 37:23
132 DBSCAN Clustering Algorithms | Density Based Clustering | How DBSCAN Works | CampusX 34:16
133 Imbalanced Data in Machine Learning | Undersampling | Oversampling | SMOTE 57:17
134 Hyperparameter Tuning using Optuna | Bayesian Optimization using Optuna 59:23

▶ يُشغَّل الآن

What is Machine Learning? | 100 Days of Machine Learning

📘 نظرة عامة على الكورس

يُقدّم هذا الكورس المجاني شرحاً شاملاً لـ 100 Days of Machine Learning من الصفر باللغة العربية في مجال الذكاء الاصطناعي وـ Machine Learning، ويستهدف المبتدئين الراغبين في بناء مهاراتهم العملية واحترافية في 2026. يُقدّمه CampusX باللغة العربية بأسلوب مبسّط ومنظّم.

يضم الكورس 134 درساً بمدة إجمالية 62 ساعة 50 دقيقة، تأخذك من الصفر حتى الاحتراف خطوةً بخطوة.

لماذا تتعلم 100 Days of Machine Learning من الصفر باللغة العربية؟

الذكاء الاصطناعي من أسرع المجالات نمواً في سوق العمل، مع زيادة الطلب على المتخصصين بنسبة 300% خلال السنوات الأخيرة.
مجاني بالكامل — ابدأ الآن دون أي رسوم
شهادة إتمام معتمدة — أضفها لـ CV وLinkedIn
تعلّم في أي وقت — بالسرعة التي تناسبك
مجالات الذكاء الاصطناعي: رؤية الحاسب، معالجة اللغة الطبيعية، الروبوتيات، الألعاب، التنبؤ والتوصية.

مسار التعلم في هذا الكورس

يسير الكورس وفق مسار منطقي متدرّج: الرياضيات والإحصاء → Python → ML → Deep Learning → Specialization

المتطلبات السابقة

Python + رياضيات (إحصاء وجبر خطي) — يمكن لأي شخص البدء في هذا الكورس.

الأدوات والبرامج المستخدمة

Python
TensorFlow
PyTorch
Scikit-learn
Jupyter
Keras
Hugging Face

متطلبات الجهاز

RAM 16GB فأكثر
GPU (NVIDIA RTX مُوصى)
SSD سريع
تخزين 500GB+

الوظائف المتاحة بعد إتمام الكورس

AI Engineer
ML Engineer
Data Scientist
AI Researcher
NLP Engineer

متوسط رواتب 100 Days of Machine Learning من الصفر باللغة العربية 2026

مصر: 12,000-40,000 ج
السعودية: 18,000-50,000 ر
أمريكا: $120,000-$200,000

كم من الوقت تحتاج لإتقان 100 Days of Machine Learning من الصفر باللغة العربية؟

أساسيات ML: 40-60 ساعة
Deep Learning: 60-100 ساعة
تخصص كامل: 200-400 ساعة

الشهادات المعتمدة في مجال الذكاء الاصطناعي وـ Machine Learning

Google ML Engineer
AWS ML Specialty
Coursera ML Specialization (Andrew Ng)
Deep Learning Specialization

مشاريع عملية ستتمكن من بناؤها

Image Classifier
Chatbot
Recommendation System
Object Detection
Sentiment Analysis

ماذا بعد هذا الكورس؟

بعد إتمام الكورس، يُوصى بالتعمق في: LLMs، Computer Vision، NLP، MLOps، Generative AI.

الأقسام الجامعية ذات الصلة

يُناسب هذا الكورس طلاب: كلية حاسبات ومعلومات، كلية هندسة (ذكاء اصطناعي)، جامعة القاهرة، جامعة عين شمس.

سجّل في كورس 100 Days of Machine Learning من الصفر باللغة العربية الآن، ابدأ رحلتك نحو الاحتراف في الذكاء الاصطناعي وـ Machine Learning مجاناً. تذكّر: أفضل وقت للتعلم هو الآن.

المدرّس / القناة

CampusX

🎬

عدد الدروس

134 درس

⏱️

المدة الإجمالية

62 ساعة 50 دقيقة

📶

المستوى

مبتدئ

🌍

اللغة

English

🎓

الشهادة

مجانية عند الإكمال

✅

المتطلبات

لا توجد متطلبات مسبقة

📂

التصنيف

الذكاء الاصطناعي وـ Machine Learning

🎯 ماذا ستتعلم في هذا الكورس

✓

What is Machine Learning? | 100 Days of Machine Learning

✓

AI Vs ML Vs DL for Beginners in Hindi

✓

Types of Machine Learning for Beginners | Types of Machine learning in Hindi | Types of ML in Depth

✓

Batch Machine Learning | Offline Vs Online Learning | Machine Learning Types

✓

Online Machine Learning | Online Learning | Online Vs Offline Machine Learning

✓

Instance-Based Vs Model-Based Learning | Types of Machine Learning

✓

Challenges in Machine Learning | Problems in Machine Learning

✓

Application of Machine Learning | Real Life Machine Learning Applications

✓

+ 126 موضوع آخر...

💼 الوظائف التي يؤهلك لها الكورس

💼

متعلق بـ الذكاء الاصطناعي وـ Machine Learning

❓ أسئلة شائعة عن الكورس

كم عدد دروس الكورس؟ +

يحتوي الكورس على 134 درس بإجمالي مدة 62 ساعة 50 دقيقة.

هل الكورس مجاني بالكامل؟ +

نعم، الكورس مجاني تماماً ومتاح بالكامل على منصة مصر 24 بدون أي رسوم.

هل الكورس مناسب للمبتدئين؟ +

الكورس موجّه لمستوى مبتدئ، وهو مناسب تماماً للمبتدئين من الصفر.

ما لغة الشرح في الكورس؟ +

الكورس مشروح باللغة الإنجليزية بأسلوب واضح ومبسّط.

هل يمكن تعلم الكورس بدون خبرة سابقة؟ +

نعم بالتأكيد. هذا الكورس مصمم للمبتدئين من الصفر.

كم يستغرق إتمام هذا الكورس؟ +

المدة الإجمالية 62 ساعة 50 دقيقة. يُنصح بتخصيص ساعة يومياً.

هل أحصل على شهادة عند الإتمام؟ +

نعم، تحصل على شهادة إتمام مجانية من مصر 24 بعد مشاهدة جميع الدروس.

من يقدم هذا الكورس؟ +

الكورس مقدم من CampusX.

EG-24 ACADEMY

EG24

EG-24ACADEMY

— أكاديمية مصر 24 —

CERTIFICATE

OF COMPLETION

THISCERTIFICATE IS PROUDLY PRESENTED TO

اسمك هنا

FOR SUCCESSFULLY COMPLETING THE COURSE

كورس 100 Days of Machine Learning

ISSUED ON

24 أغسطس 2026

Ahmed Hamdy

AHMED HAMDY

FOUNDER

VERIFY CERTIFICATE

CERTIFICATE ID

PREVIEW

رقم تحقق الشهادة

EG24

ACADEMY

LEARN GROW

🔒

شاهد 50% من الكورس واحصل على شهادتك المجانية

✅ تتبع تقدمك في الكورس

🏆 شهادة مجانية عند الإتمام

📊 إحصائياتك محفوظة دائماً

🔖 استكمل من حيث توقفت

إنشاء حساب مجاني يأخذ ثوانٍ فقط

تحقق من شهادتك

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📋 محتوى الكورس 134 درس · 62 ساعة 50 دقيقة

1 What is Machine Learning? | 100 Days of Machine Learning 20:00
2 AI Vs ML Vs DL for Beginners in Hindi 16:02
3 Types of Machine Learning for Beginners | Types of Machine learning in Hindi | Types of ML in Depth 27:42
4 Batch Machine Learning | Offline Vs Online Learning | Machine Learning Types 11:28
5 Online Machine Learning | Online Learning | Online Vs Offline Machine Learning 19:28
6 Instance-Based Vs Model-Based Learning | Types of Machine Learning 16:44
7 Challenges in Machine Learning | Problems in Machine Learning 23:40
8 Application of Machine Learning | Real Life Machine Learning Applications 29:02
9 Machine Learning Development Life Cycle | MLDLC in Data Science 25:13
10 Data Engineer Vs Data Analyst Vs Data Scientist Vs ML Engineer | Data Science Job Roles 26:23
11 What are Tensors | Tensor In-depth Explanation | Tensor in Machine Learning 41:29
12 Installing Anaconda For Data Science | Jupyter Notebook for Machine Learning | Google Colab for ML 37:06
13 End to End Toy Project | Day 13 | 100 Days of Machine Learning 30:43
14 How to Frame a Machine Learning Problem | How to plan a Data Science Project Effectively 22:22
15 Working with CSV files | Day 15 | 100 Days of Machine Learning 36:30
16 Working with JSON/SQL | Day 16 | 100 Days of Machine Learning 17:00
17 Fetching Data From an API | Day 17 | 100 Days of Machine Learning 22:50
18 Fetching data using Web Scraping | Day 18 | 100 Days of Machine Learning 37:49
19 Understanding Your Data | Day 19 | 100 Days of Machine Learning 15:23
20 EDA using Univariate Analysis | Day 20 | 100 Days of Machine Learning 30:31
21 EDA using Bivariate and Multivariate Analysis | Day 21 | 100 Days of Machine Learning 38:03
22 Pandas Profiling | Day 22 | 100 Days of Machine Learning 13:04
23 What is Feature Engineering | Day 23 | 100 Days of Machine Learning 24:52
24 Feature Scaling - Standardization | Day 24 | 100 Days of Machine Learning 32:38
25 Feature Scaling - Normalization | MinMaxScaling | MaxAbsScaling | RobustScaling 23:31
26 Encoding Categorical Data | Ordinal Encoding | Label Encoding 19:53
27 One Hot Encoding | Handling Categorical Data | Day 27 | 100 Days of Machine Learning 30:12
28 Column Transformer in Machine Learning | How to use ColumnTransformer in Sklearn 15:41
29 Machine Learning Pipelines A-Z | Day 29 | 100 Days of Machine Learning 45:39
30 Function Transformer | Log Transform | Reciprocal Transform | Square Root Transform 32:13
31 Power Transformer | Box - Cox Transform | Yeo - Johnson Transform 21:28
32 Binning and Binarization | Discretization | Quantile Binning | KMeans Binning 38:25
33 Handling Mixed Variables | Feature Engineering 12:10
34 Handling Date and Time Variables | Day 34 | 100 Days of Machine Learning 14:18
35 Handling Missing Data | Part 1 | Complete Case Analysis 24:54
36 Handling missing data | Numerical Data | Simple Imputer 31:21
37 Handling Missing Categorical Data | Simple Imputer | Most Frequent Imputation | Missing Category Imp 13:34
38 Missing Indicator | Random Sample Imputation | Handling Missing Data Part 4 37:05
39 KNN Imputer | Multivariate Imputation | Handling Missing Data Part 5 24:27
40 Multivariate Imputation by Chained Equations for Missing Value | MICE Algorithm | Iterative Imputer 18:31
41 What are Outliers | Outliers in Machine Learning 17:07
42 Outlier Detection and Removal using Z-score Method | Handling Outliers Part 2 17:46
43 Outlier Detection and Removal using the IQR Method | Handing Outliers Part 3 14:05
44 Outlier Detection using the Percentile Method | Winsorization Technique 16:23
45 Feature Construction | Feature Splitting 12:22
46 Curse of Dimensionality 15:25
47 Principle Component Analysis (PCA) | Part 1 | Geometric Intuition 33:54
48 Principle Component Analysis (PCA) | Part 2 | Problem Formulation and Step by Step Solution 56:17
49 Principle Component Analysis(PCA) | Part 3 | Code Example and Visualization 43:26
50 Simple Linear Regression | Code + Intuition | Simplest Explanation in Hindi 33:36
51 Simple Linear Regression | Mathematical Formulation | Coding from Scratch 53:31
52 Regression Metrics | MSE, MAE & RMSE | R2 Score & Adjusted R2 Score 43:56
53 Multiple Linear Regression | Geometric Intuition & Code 20:57
54 Multiple Linear Regression | Part 2 | Mathematical Formulation From Scratch 48:11
55 Multiple Linear Regression | Part 3 | Code From Scratch 16:01
56 What are the main Assumptions of Linear Regression? | Top 5 Assumptions of Linear Regression 17:38
57 Gradient Descent From Scratch | End to End Gradient Descent | Gradient Descent Animation 1:57:56
58 Batch Gradient Descent with Code Demo | Simple Explanation in Hindi 1:04:49
59 Stochastic Gradient Descent 49:35
60 Mini-Batch Gradient Descent 22:10
61 Polynomial Regression | Machine Learning 26:46
62 Bias Variance Trade-off | Overfitting and Underfitting in Machine Learning 8:05
63 Ridge Regression Part 1 | Geometric Intuition and Code | Regularized Linear Models 19:58
64 Ridge Regression Part 2 | Mathematical Formulation & Code from scratch | Regularized Linear Models 43:41
65 Ridge Regression Part 3 | Gradient Descent | Regularized Linear Models 18:43
66 5 Key Points - Ridge Regression | Part 4 | Regularized Linear Models 30:17
67 Lasso Regression | Intuition and Code Sample | Regularized Linear Models 28:37
68 Why Lasso Regression creates sparsity? 24:30
69 ElasticNet Regression | Intuition and Code Example | Regularized Linear Models 11:41
70 Logistic Regression Part 1 | Perceptron Trick 47:06
71 Logistic Regression Part 2 | Perceptron Trick Code 17:07
72 Logistic Regression Part 3 | Sigmoid Function | 100 Days of ML 40:44
73 Logistic Regression Part 4 | Loss Function | Maximum Likelihood | Binary Cross Entropy 29:03
74 Derivative of Sigmoid Function 5:57
75 Logistic Regression Part 5 | Gradient Descent & Code From Scratch 36:42
76 Accuracy and Confusion Matrix | Type 1 and Type 2 Errors | Classification Metrics Part 1 34:08
77 Precision, Recall and F1 Score | Classification Metrics Part 2 42:42
78 ROC Curve in Machine Learning | ROC-AUC in Machine Learning Simplified | CampusX 1:11:15
79 Softmax Regression || Multinomial Logistic Regression || Logistic Regression Part 6 38:21
80 Polynomial Features in Logistic Regression | Non Linear Logistic Regression | Logistic Regression 7 9:11
81 Logistic Regression Hyperparameters || Logistic Regression Part 8 13:07
82 Naive Bayes Classifier | Part 1 | Conditional Probability 9:26
83 Naive Bayes Classifier | Part 2 | Independent Events in Probability 7:59
84 Naive Bayes Classifier | Part 3 | Mutually Exclusive Events 1:49
85 Naive Bayes Classifier | Part 4 | Bayes Theorem in Probability 4:27
86 Naive Bayes Classifier | Part 5 | Problem based upon Bayes Theorem 9:00
87 Naive Bayes Classifier | Part 6 | Intuition 14:44
88 Naive Bayes Classifier | Part 7 | Mathematics behind Naive Bayes Algorithm 19:09
89 Naive Bayes Classifier | Part 8 | Simple Example Code 16:03
90 Naive Bayes Part 9 | Handling Numerical Data 8:47
91 What is K Nearest Neighbors? | KNN Explained in Hindi | Simple Overview in 1 Video | CampusX 52:01
92 Support Vector Machines | Geometric Intuition 11:46
93 Mathematics of SVM | Support Vector Machines | Hard margin SVM 34:54
94 Mathematics of Support Vector Machine | Soft Margin SVM 14:38
95 Kernel Trick in SVM | Geometric Intuition 6:18
96 Kernel Trick in SVM | Code Example 14:04
97 Decision Trees Geometric Intuition | Entropy | Gini impurity | Information Gain 58:29
98 Decision Trees - Hyperparameters | Overfitting and Underfitting in Decision Trees 27:23
99 Regression Trees | Decision Trees Part 3 35:15
100 Awesome Decision Tree Visualization using dtreeviz library 18:36
101 Introduction to Ensemble Learning | Ensemble Techniques in Machine Learning 37:43
102 Voting Ensemble | Introduction and Core Idea | Part 1 16:30
103 Voting Ensemble | Classification | Voting Classifier | Hard Voting Vs Soft Voting | Part 2 23:50
104 Voting Ensemble | Regression | Part 3 10:57
105 Bagging | Introduction | Part 1 31:13
106 Bagging Ensemble | Part 2 | Bagging Classifiers 22:32
107 Bagging Ensemble | Part 3 | Bagging Regressor 10:55
108 Introduction to Random Forest | Intuition behind the Algorithm 33:55
109 How Random Forest Performs So Well? Bias Variance Trade-Off in Random Forest 12:52
110 Bagging Vs Random Forest | What is the difference between Bagging and Random Forest | Very Important 12:02
111 Random Forest Hyper-parameters 15:17
112 Hyperparameter Tuning Random Forest using GridSearchCV and RandomizedSearchCV | Code Example 11:44
113 OOB Score | Out of Bag Evaluation in Random Forest | Machine Learning 6:45
114 Feature Importance using Random Forest and Decision Trees | How is Feature Importance calculated 27:20
115 How Adaboost Classifier Works? | Geometric Intuition 17:14
116 AdaBoost - A Step by Step Explanation 19:23
117 AdaBoost Algorithm | Code from Scratch 16:27
118 AdaBoost Hyperparameters | GridSearchCV in Adaboost 11:13
119 Bagging Vs Boosting | What is the difference between Bagging and Boosting 6:17
120 Gradient Boosting Explained | How Gradient Boosting Works? 32:49
121 Gradient Boosting Regression Part 2 | Mathematics of Gradient Boosting 56:42
122 Gradient Boosting for Classification | Geometric Intuition | CampusX 1:04:33
123 Introduction to XGBOOST | Machine Learning | CampusX 1:19:37
124 XGBoost for Regression | XGBoost Part 2 | CampusX 47:17
125 XGBoost For Classification | How XGBoost works on Classification Problems | CampusX 39:08
126 The Maths Behind XGBoost | Machine Learning | CampusX 1:57:28
127 Stacking and Blending Ensembles 35:20
128 K-Means Clustering Algorithm | Geometric Intuition | Clustering | Unsupervised Learning 23:58
129 K-Means Clustering Algorithm in Python | Practical Example | Student Clustering Example | sklearn 10:13
130 K-Means Clustering Algorithm From Scratch In Python | ML Algorithms From Scratch 33:53
131 Agglomerative Hierarchical Clustering | Python Code Example 37:23
132 DBSCAN Clustering Algorithms | Density Based Clustering | How DBSCAN Works | CampusX 34:16
133 Imbalanced Data in Machine Learning | Undersampling | Oversampling | SMOTE 57:17
134 Hyperparameter Tuning using Optuna | Bayesian Optimization using Optuna 59:23

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