الرئيسية
الدورات التدريبية
تواصل معنا
الرئيسية
الدورات التدريبية
تواصل معنا
تواصل معنا
منهاج
24 Sections
64 Lessons
10 Weeks
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Intro
4
1.1
What is the meaning of Machine Learning
1.2
How can i get Dataset Part 1
1.3
How can i get Dataset Part 2
1.4
How can i get Dataset Part 3
Master Data Preprocessing
7
2.1
ما قبل بناء اي موديل بنقسم البيانات ازاي ؟
2.2
Data Preprocessing Part 1
2.3
Data Preprocessing Part 2
2.4
Data Preprocessing Part 3 – Normalization and Standardization
2.5
Feature Scalling with code
2.6
Data Cleaning
2.7
Data Cleaning With Code
Simple linear Regression
7
3.1
Simple Linear Regression
3.2
Simple Linear Regression with code
3.3
OLS (Ordinary Least Squares)
3.4
How to evaluate Performace of regression Model – Cost Function
3.5
How to evaluate Performace of regression Model – Cost Function Part 2
3.6
Gradiant Decent Part 1
3.7
Gradiant Decent Part 2
Multiple linear regression
3
4.1
Multiple Linear Regression
4.2
Multiple linear regression with code part 1
4.3
Multiple linear regression with code part 2
Polynomial regression
2
5.1
Polynomial regression
5.2
Polynomial regression With code
Overfitting & Underfitting
1
6.1
Overfitting & Underfitting
Regularization
1
7.1
(L2) Regularization – Lasso (L1) & Ridge
Cross-Validations
2
8.1
Types of Cross-Validations
8.2
Types of Cross-Validations With Code
Top Evaluation Metrics for Regression Problems in Machine Learning
1
9.1
Evaluation Metrics for Regression Problems
Decision Tree in regression
1
10.1
Decision Tree in Regression
Ensemble Models
1
11.1
Bagging
Random Foreset in Regression
1
12.1
Random Foreset in Regression
Ensemble Models
3
13.1
Boosting
13.2
AdaBoost
13.3
Gradient Boosting Machine (GBM)
Black Friday Project
6
14.1
Part 1
14.2
Part 2
14.3
Part 3
14.4
Part 4
14.5
Decision Tree
14.6
Random Forest
Classification Modules
6
15.1
Intro to Classification Models
15.2
Decision Tree (For Classification)
15.3
Random Forest Algorithm (For Classification)
15.4
Logistic Regression
15.5
Naive Bayes
15.6
k-Neares-Neighbor
Support Vector Machine in Classification
5
16.1
SVM intro
16.2
Hard Margin
16.3
Soft Margin
16.4
Kernel Trick
16.5
SVM with Code
Top Evaluation Metrics for Classification Problems in Machine Learning
1
17.1
Evaluation Metrics for Classification Problems
Balancing Data
2
18.1
Different Methods for Balancing Data in Classification Problems
18.2
ADASYN with Code
K-Means Cluster
3
19.1
K-Means Cluster
19.2
K-Means Cluster Part 2
19.3
K-Means Cluster Part 3 With Code
Hierarchical Clustering
1
20.1
Hierarchical Clustering
DBSCAN
1
21.1
DBSCAN
Principal Component Analysis (PCA)
2
22.1
Principal Component Analysis (PCA)
22.2
Principal Component Analysis (PCA) With Code
Feature Selection
1
23.1
Feature Selection Methods
Time Series
2
24.1
Time Series
24.2
Time Series With Code
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