https://github.com/junaidsalim/machine_learning_a-z
This repository contains Python implementations of various machine learning models that I studied during the Machine Learning A-Z course.
https://github.com/junaidsalim/machine_learning_a-z
association associative-learning data-science datapreprocessing jupyter-notebook machine-learning machinelearning machinelearning-python nlp python regression reinforcement-learning
Last synced: 3 months ago
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This repository contains Python implementations of various machine learning models that I studied during the Machine Learning A-Z course.
- Host: GitHub
- URL: https://github.com/junaidsalim/machine_learning_a-z
- Owner: JunaidSalim
- License: mit
- Created: 2024-02-01T17:50:43.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-18T06:07:56.000Z (about 1 year ago)
- Last Synced: 2025-07-04T00:04:14.040Z (3 months ago)
- Topics: association, associative-learning, data-science, datapreprocessing, jupyter-notebook, machine-learning, machinelearning, machinelearning-python, nlp, python, regression, reinforcement-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 5.95 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Machine Learning A-Z
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 Convolutional Neural Networks (CNN)
# Repository Structure
```
Machine Learning A-Z/
├── Model Selection/
│ ├── Classification
│ └── Regression
├── Part 1 - Data Preprocessing
├── Part 2 - Regression/
│ ├── Section 4 - Simple Linear Regression
│ ├── Section 5 - Multiple Linear Regression
│ ├── Section 6 - Polynomial Regression
│ ├── Section 7 - Support Vector Regression (SVR)
│ ├── Section 8 - Decision Tree Regression
│ └── Section 9 - Random Forest Regression
├── Part 3 - Classification/
│ ├── Section 14 - Logistic Regression
│ ├── Section 15 - K-Nearest Neighbors (K-NN)
│ ├── Section 16 - Support Vector Machine (SVM)
│ ├── Section 17 - Kernel SVM
│ ├── Section 18 - Naive Byes
│ ├── Section 19 - Decision Tree Classification
│ └── Section 20 - Random Forest Classification
├── Part 4 - Clustering/
│ ├── Section 24 - K-Means Clustering
│ └── Section 25 - Heirarchical Clustering
├── Part 5 - Association Rule Learning/
│ ├── Section 28 - Apriori
│ └── Section 29 - Eclat
├── Part 6 - Reinforcement Learning/
│ ├── Section 32 - Upper Confidence Bound (UCB)
│ └── Section 33 - Thompson Sampling
├── Part 7 - Natural Language Processing
├── Part 8 - Deep Learning/
│ ├── Section 39 - Artificial Neural Network (ANN)
│ └── Section 40 - Convolutional Neural Network (CNN)
├── Part 9 - Dimensionality Reduction/
│ ├── Section 43 - Principal Component Analysis (PCA)
│ ├── Section 44 - Linear Discriminant Analysis (LDA)
│ └── Section 45 - Kernel PCA
└── Part 10 - Model Selection and Boosting/
├── Section 48 - Model Selection
├── Section 49 - XGBoost
└── Section 50 - CatBoost
```
# [Course Certificate](https://www.udemy.com/certificate/UC-fd154b0b-1ef0-4899-83f6-2ecea2dafbf7/)
