The theoretical and practical part of Principal Component Analysis with python implementation

Table of Contents
1. Introduction
2. Principal Component Analysis (PCA)
3. Theory
3.1. Calculating PCA
3.1.1. Rescaling (Standardization)
3.1.2. Covariance Matrix
3.1.3. Eigenvalues and Eigenvectors
3.1.4. Sorting in Descent Order
3.2. Is PCA one of the feature extraction&feature selection methods?
4. Implementation
4.1. Traditional Machine Learning Approaches
4.2. Deep Learning Approaches
5. PCA Types
5.1. Kernel PCA
5.2. Sparse PCA
5.3. Randomized PCA
5.4. Incremental PCA

Exploring Poisson distribution and predicting Galatasaray vs Fenerbahce match result using it with python implementation

Thoughts and Theory

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Applying Ensemble Learning Algorithms to the image dataset that are features extracted by Convolutional Layers with a python implementation

Table of Contents
1. Introduction
2.
Layers
2.1. Convolutional Layer
2.2. Pooling Layer
2.3. Dropout Layer
2.4. Flatten Layer
3.
Tutorial
3.1. Dense Layer Approach
3.2. Ensemble Learning Approach
4. Results
5.
Discussion

1. Introduction

It is mostly converted into (n_samples, n_features) and the algorithm is applied, after the necessary data preprocessing…

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Explaining the p-value by explaining statistical terms and experiment with a simple fact which the case where 75% accuracy is statistically insignificant

Ibrahim Kovan

MSc -Biomedical Engineering - Data Science and Machine Learning https://www.linkedin.com/in/ibrahimkovan/

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