The Complete Machine Learning Bootcamp – 2025

Teacher

Norhan Zaid

Review

مجاني

Course Features

Lectures 64
Quiz 0
Duration 10 weeks
Skill level All levels
Language English
Students 0
Assessments Yes

منهاج

24 Sections
64 Lessons
10 Weeks

Expand all sectionsCollapse all sections

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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