https://github.com/junaidsalim/ml_practice
This repository features Python implementations of a wide range of machine learning models that I explored during the Machine Learning A-Z course. The models cover Regression, Classification, Clustering, Reinforcement Learning, Association Rule Learning, Natural Language Processing (NLP), as well as Artificial Neural Networks (ANN) and Convolutiona
https://github.com/junaidsalim/ml_practice
jupyter-notebook machine-learning machinelearning-practice python
Last synced: about 2 months ago
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This repository features Python implementations of a wide range of machine learning models that I explored during the Machine Learning A-Z course. The models cover Regression, Classification, Clustering, Reinforcement Learning, Association Rule Learning, Natural Language Processing (NLP), as well as Artificial Neural Networks (ANN) and Convolutiona
- Host: GitHub
- URL: https://github.com/junaidsalim/ml_practice
- Owner: JunaidSalim
- License: mit
- Created: 2024-02-05T05:05:36.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-05T08:13:46.000Z (10 months ago)
- Last Synced: 2025-02-11T13:52:26.754Z (4 months ago)
- Topics: jupyter-notebook, machine-learning, machinelearning-practice, python
- Language: Jupyter Notebook
- Homepage:
- Size: 9.07 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ML_Practice
This repository contains implementations of various machine learning models applied to different real-world datasets. I created this repository for practice, which helped me hone my machine learning skills.
# Repository Structure
```
ML_Practice/
├── 1.Data Preprocessing/
│ ├── 1.Importing Datasets
│ ├── 2.Dealing with null values
│ ├── 3.Data Formatting & Data Binning
│ └── 4.Dealing With Categorical Values
├── 2.Regression/
│ ├── 1.Simple Linear Regression
│ ├── 2.Multiple Linear Regression
│ ├── 3.Polynomial Regression
│ ├── 4.Support Vector Regression (SVR)
│ ├── 5.Decision Tree Regression
│ └── 6.Random Forest Regression
├── 3.Classification/
│ ├── 1.Logistic Regression
│ ├── 2.K-Nearest Neighbors (K-NN)
│ ├── 3.Support Vector Machine (SVM)
│ ├── 4.Kernel SVM
│ ├── 5.Naive Byes
│ ├── 6.Decision Tree Classification
│ └── 7.Random Forest Classification
├── 4.Clustering/
│ ├── 1.K-Means Clustering
│ └── 2.Heirarchical Clustering
├── 5.Association Rule Learning/
│ ├── 1.Apriori
│ └── 2.Eclat
├── 6.Reinforcement Learning/
│ ├── 1.Upper Confidence Bound (UCB)
│ └── 2.Thompson Sampling
├── 7.Natural Language Processing
├── 8.Deep Learning/
│ ├── 1.Artificial Neural Network (ANN)
│ └── 2.Convolutional Neural Network (CNN)
├── 9.Dimensionality Reduction/
│ ├── 1.Principal Component Analysis (PCA)
│ ├── 2.Linear Discriminant Analysis (LDA)
│ └── 3.Kernel PCA
├── 10.Model Selection and Boosting/
│ ├── 1.Model Selection
│ ├── 2.XGBoost
│ └── 3.CatBoost
└── Model Selection/
├── Classification
└── Regression
```