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Machine Learning
Introduction
1- Intro
2- What is Machine Learning?
Chapter 1
1- Categories Of ML Part 1
2- Categories Of ML Part 2
3- Categories Of ML Part 3
4- Supervised Learning
5- Unsupervised Learning
6- Unsupervised Learning ( Dimensionality Reduction )
7- Reinforcement
Chapter 2
1- Simple Linear Regression
2- Polynomial Regression
3- Multi-Linear Regression
4- SVM Regression
5- Decision Tree Regression
6- Random Forest Regression
Chapter 3
1- Logistic Regression
2- Decision Tree Classification
3- SVM Classification
4- Random Forest Classification
5- Naive Bayes Classification
Chapter 4
1- Clustering
2- K Means Clustering
3- Hierarchical Clustering
Chapter 5
1- Declaration Part 1
2- Declaration Part 2
3- Declaration Part 3
Chapter 6
1- Fitting Issues
2- Confusion Matrix - Accuracy
3- Precision and Recall
4- F1 Score
5- F-Beta Score
6- Logarithmic Loss
7- Mean Absolute Error
Chapter 7
1- Random W to Perfect W
2- Gradient Descent
3- Gradient Descent & Learning Rate
4- Issues with Gradient Descent
5- Momentum
Chapter 8
1- Forward Propagation
2- Back Propagation
Chapter 9
1- Data Types & Issues
2- Categorical Data Encoding
Chapter 10
Lesson 01
Lesson 02
Basic Models
Basic Models
Lesson 01
Lesson 02
Lesson 03
Advanced Models
Lesson 01
Lesson 02
3- Pipeline
4- Search and Pipeline
Linear Regression
Linear Regression Intro
Linear Regression From Scratch
KNN
KNN Intro
KNN
Support Vector Machine
SVM
SVM from Scratch
Gradient Search
Gredient
Models Optimization
Regression Evaluation
Model Optimization
Model Evaluation
Grid Search Optimization
Framework Development
All Models
Preparation
Preparation Part 01
Preparation Part 02
Preparation Part 01
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