https://github.com/asifhaider/machine-learning-4-2

Machine Learning Course Assignments from Scratch (Exploratory Data Analysis, Logistic Regression, Adaboost, Feed Forward Neural Network, EM Algorithm, Gaussian Mixture Models, Singular Value Decomposition, Image Reconstruction, Principal Component Analysis)
https://github.com/asifhaider/machine-learning-4-2

adaboost eda em-algorithm gaussian-mixture-models logistic-regression neural-network pca-analysis preprocessing singular-value-decomposition

Last synced: 10 months ago
JSON representation

Machine Learning Course Assignments from Scratch (Exploratory Data Analysis, Logistic Regression, Adaboost, Feed Forward Neural Network, EM Algorithm, Gaussian Mixture Models, Singular Value Decomposition, Image Reconstruction, Principal Component Analysis)

• Host: GitHub
• URL: https://github.com/asifhaider/machine-learning-4-2
• Owner: asifhaider
• Created: 2023-12-12T13:51:02.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-07-13T17:03:45.000Z (over 1 year ago)
• Last Synced: 2025-02-01T22:47:57.750Z (12 months ago)
• Topics: adaboost, eda, em-algorithm, gaussian-mixture-models, logistic-regression, neural-network, pca-analysis, preprocessing, singular-value-decomposition
• Language: Jupyter Notebook
• Homepage:
• Size: 6.59 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Machine-Learning-Course-Assignments

## Assignment 1: Linear Algebra for Machine Learning

* **Matrix Transformation**

    - [Notebook Link](/Linear-Algebra-For-ML/matrix-transformations-and-eigen-decomposition.ipynb)

* **Eigen Decomposition**

    - Decomposition and reconstruction of [invertible](/Linear-Algebra-For-ML/random_eigen.py) and [invertible symmetric](/Linear-Algebra-For-ML/symmetric_eigen.py) matrices

![svd image](/Linear-Algebra-For-ML/svd.png)

* **Singular Value Decomposition**

    - Reconstructing a book cover image using [singular value decomposition](/Linear-Algebra-For-ML/image_reconstruction.py) with varying ranks

## Assignment 2: Logistic Regression and Adaboost for Classification

- **Data Preprocessing**

![1](/Logistic-Regression-Adaboost/1.png)

    - [Telco Customer Churn Notebook](/Logistic-Regression-Adaboost/eda-1.ipynb)

![2b](/Logistic-Regression-Adaboost/2b.png) 

    - [Census Income Train Notebook](/Logistic-Regression-Adaboost/eda-2a.ipynb), [Census Income Test Notebook](/Logistic-Regression-Adaboost/eda-2b.ipynb)

![3](/Logistic-Regression-Adaboost/3.png)

    - [Credit Card Fraud Notebook](/Logistic-Regression-Adaboost/eda-3.ipynb)

- **Adaboost with Logistic Regression from Scratch**

    - [Python Code](/Logistic-Regression-Adaboost/1805112.py)

- **Results and Discussion**

    - [Report Link](/Logistic-Regression-Adaboost/1805112.pdf)

## Assignment 3: Feed Forward Neural Network from Scratch

![Model Layers](/Neural-Network-From-Scratch/fnn_1.png)

- Layer Abstractions (Dense, ReLU, Softmax, Dropout) with Forward and Backward Propagations: [Neural Network Backbone Code](/Neural-Network-From-Scratch/nn_1805112.py)

![Training Photo](/Neural-Network-From-Scratch/train-eval-loss.png)

- Loss function, Evaluation Metric, Loading, Preprocessing, Training, Evaluation and Plotting: [Training Code](/Neural-Network-From-Scratch/train_1805112.py)

![Results](/Neural-Network-From-Scratch/confusion.png)

- Inference: [Testing Code](/Neural-Network-From-Scratch/test_1805112.py)

- [Report Link](/Neural-Network-From-Scratch/report_1805112.pdf)

## Assignment 4: Expectation-Maximization (EM) Algorithm with Gaussian Mixture Models (GMM)

![2D1](/Gaussian-Mixture-Models-EM-Algo//2D_data_points_1_GMM_3.png) ![2D2](/Gaussian-Mixture-Models-EM-Algo/2D_data_points_2_GMM_5.png) ![3D](/Gaussian-Mixture-Models-EM-Algo/3D_data_points_GMM_4.png) ![6D](/Gaussian-Mixture-Models-EM-Algo/6D_data_points_GMM_5.png) ![100D](/Gaussian-Mixture-Models-EM-Algo/100D_data_points_GMM_3.png)

- Principal Component Analysis (PCA), Expectation-Maximization Algorithm, Log-likelihood Calculation: [Python Code](/Gaussian-Mixture-Models-EM-Algo/1805112.py)